Tripitaka MCP

io.github.dhamma-seeker/tripitaka-mcpVersion 1.3.0

MCP server for the full Pāli Canon — search, cite, compare translations. Offered as Dhamma Dāna.

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Last checked: Nov 14, 2026, 14:49 UTC
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https://mcp.tripitaka-mcp.com/mcpResponds182 msNov 14, 2026, 14:49 UTC

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Claude Code

claude mcp add --transport http tripitaka-mcp https://mcp.tripitaka-mcp.com/mcp

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: tripitaka-mcp
Remote MCP server URL: https://mcp.tripitaka-mcp.com/mcp

Cursor

{
  "mcpServers": {
    "tripitaka-mcp": {
      "url": "https://mcp.tripitaka-mcp.com/mcp"
    }
  }
}

VS Code

code --add-mcp '{"name":"tripitaka-mcp","type":"http","url":"https://mcp.tripitaka-mcp.com/mcp"}'

SSE

https://mcp.tripitaka-mcp.com/sseNot checkedUses SSE

Uses SSE (legacy), not checked

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Tools

14 tools

Tools of Tripitaka MCP
	Description		Read-only	Idempotent	Open world
`compare_translations`Compare every available translation for a single segment. 💡 Use this tool when: - The user asks about the meaning/translation of a single Pāli line and wants to see multiple translators side-by-side. - Checking how different translators interpret the same line — technical terms like `dukkha`, `anattā`, `nibbāna` carry nuance that varies across translations. - Academic work that needs to quote multiple translations. 🔍 vs `get_sutta`: this tool targets a single segment (line level); `get_sutta` returns the whole sutta. To compare a whole sutta you'd call `compare_translations` for each segment. 📋 segment_id format: `<sutta_id>:<paragraph>.<line>`, e.g. `mn1:171.4` (Mūlapariyāyasutta paragraph 171 line 4 — "Nandī dukkhassa mūlaṁ"). Find segment_ids via `get_sutta` or search results. ⚠️ Current state: the `translation` table is mostly empty (the DB only loads default Pāli + English from bilara). `total_editions` is usually 0; `text_pali` and `text_english` are always populated. Thai editions will be added later.	Compare every available translation for a single segment. 💡 Use this tool when: - The user asks about the meaning/translation of a single Pāli line and wants to see multiple translators side-by-side. - Checking how different translators interpret the same line — technical terms like `dukkha`, `anattā`, `nibbāna` carry nuance that varies across translations. - Academic work that needs to quote multiple translations. 🔍 vs `get_sutta`: this tool targets a single segment (line level); `get_sutta` returns the whole sutta. To compare a whole sutta you'd call `compare_translations` for each segment. 📋 segment_id format: `<sutta_id>:<paragraph>.<line>`, e.g. `mn1:171.4` (Mūlapariyāyasutta paragraph 171 line 4 — "Nandī dukkhassa mūlaṁ"). Find segment_ids via `get_sutta` or search results. ⚠️ Current state: the `translation` table is mostly empty (the DB only loads default Pāli + English from bilara). `total_editions` is usually 0; `text_pali` and `text_english` are always populated. Thai editions will be added later.	No	Yes	Yes	No
`define_from_suttas`Find how the suttas and Vinaya define a Pāli term in their own words. The canon defines its own terms with fixed formulas — "Katamañca … dukkhaṁ?" (what is X?) … "ayaṁ vuccati … dukkhaṁ" (this is called X), "X adhivacana" (X is a designation for …), or the Vinaya "X nāma". This tool locates those definitional passages and returns them cited, so the assistant can present the doctrinal essence straight from the source. 🧭 This tool vs `get_word_definition`: - `define_from_suttas` → the doctrinal definition, how the term is defined inside the canon. Use for "how do the suttas define X", "what is the canonical definition of X", "define X from the suttas". Returns a few precise segments, not a lexicon essay. - `get_word_definition` → the lexical definition from dictionaries (Payutto / PTS / DPPN). Use for etymology and word meaning. They complement each other — offer both when the user wants the full picture (dictionary sense + how the Buddha defined it). 📖 How to present the result: Results are ranked; the top one is usually the canonical definition. Quote the Pāli (and English where present) verbatim and render each `cross_reference.tripitaka_mcp_reader.segment_url` as clickable markdown so the user can verify. Do NOT paraphrase into your own definition — the point is the canon's own words. Each result is tagged `kind` (direct / simile) and `detail` (descriptive / enumerative); a descriptive definition characterises the term, an enumerative one lists its types — prefer the descriptive when explaining the essence. ⚠️ A result tagged `context: true` does not contain the term in its own line. The canon's stock similes attach to a formula rather than to a word: the four jhāna similes (bath powder, deep lake, lotus pond, white cloth) never say jhāna, they illustrate the `vivicceva kāmehi …` formula that opens the paragraph. Such rows are found through that paragraph, so say so when quoting one — present it as th	Find how the suttas and Vinaya define a Pāli term in their own words. The canon defines its own terms with fixed formulas — "Katamañca … dukkhaṁ?" (what is X?) … "ayaṁ vuccati … dukkhaṁ" (this is called X), "X adhivacana" (X is a designation for …), or the Vinaya "X nāma". This tool locates those definitional passages and returns them cited, so the assistant can present the doctrinal essence straight from the source. 🧭 This tool vs `get_word_definition`: - `define_from_suttas` → the doctrinal definition, how the term is defined inside the canon. Use for "how do the suttas define X", "what is the canonical definition of X", "define X from the suttas". Returns a few precise segments, not a lexicon essay. - `get_word_definition` → the lexical definition from dictionaries (Payutto / PTS / DPPN). Use for etymology and word meaning. They complement each other — offer both when the user wants the full picture (dictionary sense + how the Buddha defined it). 📖 How to present the result: Results are ranked; the top one is usually the canonical definition. Quote the Pāli (and English where present) verbatim and render each `cross_reference.tripitaka_mcp_reader.segment_url` as clickable markdown so the user can verify. Do NOT paraphrase into your own definition — the point is the canon's own words. Each result is tagged `kind` (direct / simile) and `detail` (descriptive / enumerative); a descriptive definition characterises the term, an enumerative one lists its types — prefer the descriptive when explaining the essence. ⚠️ A result tagged `context: true` does not contain the term in its own line. The canon's stock similes attach to a formula rather than to a word: the four jhāna similes (bath powder, deep lake, lotus pond, white cloth) never say jhāna, they illustrate the `vivicceva kāmehi …` formula that opens the paragraph. Such rows are found through that paragraph, so say so when quoting one — present it as th	No	Yes	Yes	No
`get_reference`Build a proper citation string for a sutta. 💡 Use this tool when: - The user wants a citation for academic work, an article, or a reference. - You need to know the canonical location of a sutta (pitaka / nikāya). - You want a ready-to-use formatted citation string. 🔗 vs `get_sutta`: this tool returns metadata + citation only, no segments. Pair it with `get_sutta` when you want both the content and the citation.	Build a proper citation string for a sutta. 💡 Use this tool when: - The user wants a citation for academic work, an article, or a reference. - You need to know the canonical location of a sutta (pitaka / nikāya). - You want a ready-to-use formatted citation string. 🔗 vs `get_sutta`: this tool returns metadata + citation only, no segments. Pair it with `get_sutta` when you want both the content and the citation.	No	Yes	Yes	No
`get_sutta`Fetch a sutta's content — OR its table of contents (`mode="outline"`). ⚡ Decide which mode BEFORE calling — don't fetch the whole sutta and parse it yourself: - The user wants the structure / outline / table of contents, or asks "how many sections/parts" / "what's in it" → call `get_sutta(sutta_id, mode="outline")`. It returns the section list (titles + segment counts + ids), NOT the full text — cheap and exact. - The user wants the context around a search hit → `around="<segment_id>"` (search tools hand you the id, e.g. `dn22:18.1`) + optional `window`. - The user wants a specific part you already located → `segment_range="A..B"` or `offset`+`limit`. - Only fetch the whole sutta (no mode/selector) when the user actually wants to read/quote a SHORT sutta in full. Long ones (DN, long Vinaya/Abhidhamma; > ~400 segments — e.g. `dn16` is 1,664) should almost always start with `mode="outline"`; pulling the entire text wastes the context window. Uses standard SuttaCentral IDs, e.g.: - `mn1` = Majjhima Nikāya sutta 1 (Mūlapariyāyasutta, 334 segments) - `dn22` = Dīgha Nikāya sutta 22 (Mahāsatipaṭṭhānasutta, 454 segments) - `dn16` = Dīgha Nikāya sutta 16 (Mahāparinibbānasutta — the longest sutta in the canon, 1,664 segments) - `sn56.11` = Saṃyutta 56.11 (Dhammacakkappavattana) - `mn62` = Majjhima Nikāya 62 (Mahārāhulovāda — advice to Rāhula) - `dhp1-20` = Dhammapada verses 1-20 (KN uses range format) - `mil3.1.1` = Milindapañha 3.1.1 (paracanonical, 3–4 level id) 💡 Hints for the AI client: - Quote `text_pali` / `text_english` directly from the returned segments — do not rely on training memory. The system is verifiable; AI recall is often wrong. - Short segments numbered `:0.n` are headers, not the teaching itself — actual content starts around `:1.1`. They run collection → book → chapter → sutta, so the last one is the sutta's own name (`sn35.245:0.3` = Kiṁsukopamasutta, while `:0.2` is its chapter).	Fetch a sutta's content — OR its table of contents (`mode="outline"`). ⚡ Decide which mode BEFORE calling — don't fetch the whole sutta and parse it yourself: - The user wants the structure / outline / table of contents, or asks "how many sections/parts" / "what's in it" → call `get_sutta(sutta_id, mode="outline")`. It returns the section list (titles + segment counts + ids), NOT the full text — cheap and exact. - The user wants the context around a search hit → `around="<segment_id>"` (search tools hand you the id, e.g. `dn22:18.1`) + optional `window`. - The user wants a specific part you already located → `segment_range="A..B"` or `offset`+`limit`. - Only fetch the whole sutta (no mode/selector) when the user actually wants to read/quote a SHORT sutta in full. Long ones (DN, long Vinaya/Abhidhamma; > ~400 segments — e.g. `dn16` is 1,664) should almost always start with `mode="outline"`; pulling the entire text wastes the context window. Uses standard SuttaCentral IDs, e.g.: - `mn1` = Majjhima Nikāya sutta 1 (Mūlapariyāyasutta, 334 segments) - `dn22` = Dīgha Nikāya sutta 22 (Mahāsatipaṭṭhānasutta, 454 segments) - `dn16` = Dīgha Nikāya sutta 16 (Mahāparinibbānasutta — the longest sutta in the canon, 1,664 segments) - `sn56.11` = Saṃyutta 56.11 (Dhammacakkappavattana) - `mn62` = Majjhima Nikāya 62 (Mahārāhulovāda — advice to Rāhula) - `dhp1-20` = Dhammapada verses 1-20 (KN uses range format) - `mil3.1.1` = Milindapañha 3.1.1 (paracanonical, 3–4 level id) 💡 Hints for the AI client: - Quote `text_pali` / `text_english` directly from the returned segments — do not rely on training memory. The system is verifiable; AI recall is often wrong. - Short segments numbered `:0.n` are headers, not the teaching itself — actual content starts around `:1.1`. They run collection → book → chapter → sutta, so the last one is the sutta's own name (`sn35.245:0.3` = Kiṁsukopamasutta, while `:0.2` is its chapter).	No	Yes	Yes	No
`get_word_definition`Look up the dictionary meaning of a Pāli word, with sutta context. Serves as a Pāli Dictionary Bridge — pairs the "definition" with the "context where the Buddha actually used the word". 📖 About the dictionary sources: This tool draws from multiple primary dictionaries, including "พจนานุกรมพุทธศาสน์ ฉบับประมวลศัพท์" (Buddhist Dictionary — Concept-Glossary edition) by Somdet Phra Buddhaghosacariya (P. A. Payutto). The Thai-language entries are original scholarly works (not translations), so they are always available even when ENABLED_LANGUAGES has Thai disabled. The AI client should translate Thai entries into the user's language if needed. 🔀 Two kinds of thing in one payload. `definitions[]` come from the dictionaries, not the canon, so they carry no `segment_id`; `appears_in_context[]` is corpus text and does carry one. `related_words[]` are headwords holding your query as a substring, so opposites appear — `dhamma` returns `adhamma`. Context examples are drawn at random: two identical calls return different ones.	Look up the dictionary meaning of a Pāli word, with sutta context. Serves as a Pāli Dictionary Bridge — pairs the "definition" with the "context where the Buddha actually used the word". 📖 About the dictionary sources: This tool draws from multiple primary dictionaries, including "พจนานุกรมพุทธศาสน์ ฉบับประมวลศัพท์" (Buddhist Dictionary — Concept-Glossary edition) by Somdet Phra Buddhaghosacariya (P. A. Payutto). The Thai-language entries are original scholarly works (not translations), so they are always available even when ENABLED_LANGUAGES has Thai disabled. The AI client should translate Thai entries into the user's language if needed. 🔀 Two kinds of thing in one payload. `definitions[]` come from the dictionaries, not the canon, so they carry no `segment_id`; `appears_in_context[]` is corpus text and does carry one. `related_words[]` are headwords holding your query as a substring, so opposites appear — `dhamma` returns `adhamma`. Context examples are drawn at random: two identical calls return different ones.	No	Yes	Yes	No
`list_editions`List the translation editions available, with coverage stats. 💡 Use this tool when: - Before calling `compare_translations` or `get_sutta(edition=...)`, so you know which edition values are valid and worth comparing. - The user asks which editions are loaded in the DB. 🔍 Filtering: Filtered by the server's `TRIPITAKA_ENABLED_LANGUAGES` — when Thai is disabled the list is empty. Only enabled languages are returned. ⚠️ Current state: the DB mostly holds Pāli (default from SuttaCentral bilara) and English (bilara's translators). Thai editions (`dhiranandi`, `jayasaro`, `mbu`, `royal`) aren't indexed yet — the list returns empty until they're loaded.	List the translation editions available, with coverage stats. 💡 Use this tool when: - Before calling `compare_translations` or `get_sutta(edition=...)`, so you know which edition values are valid and worth comparing. - The user asks which editions are loaded in the DB. 🔍 Filtering: Filtered by the server's `TRIPITAKA_ENABLED_LANGUAGES` — when Thai is disabled the list is empty. Only enabled languages are returned. ⚠️ Current state: the DB mostly holds Pāli (default from SuttaCentral bilara) and English (bilara's translators). Thai editions (`dhiranandi`, `jayasaro`, `mbu`, `royal`) aren't indexed yet — the list returns empty until they're loaded.	No	Yes	Yes	No
`list_structure`Show the structure of all three pitakas with coverage statistics. 💡 Use this tool when: - The user asks for an overview of the Tipiṭaka (what's in it / which collections). - You need to check coverage before promising a search will find something — `segment_count > 0` is the active-loaded signal. - Verifying scope when compiling an artifact. 📊 Current state (v1.1+, at parity with SuttaCentral bilara-data): - Sutta Piṭaka complete: DN 37, MN 155, SN 1,829, AN 1,419, KN 2,351 sections (~284,702 segments) — Pāli + bilara EN (Sujato for DN/MN/SN/AN and most of KN; a few KN books have other bilara translators) - Vinaya Piṭaka complete: Bhikkhu Vibhaṅga 222, Bhikkhunī Vibhaṅga 127, Khandhaka 22, Parivāra 51 + Pātimokkha 2 (~71,557 segments) — Pāli + Brahmali EN - Abhidhamma Piṭaka complete: 7 books (ds, vb, dt, pp, kv, ya, patthana) ~88,414 segments — Pāli only (bilara has no English for any Abhidhamma book) - Total ~444,673 segments in the DB ⚠️ Known quirks: - The schema carries duplicate legacy + SC-modern codes side by side: - Vinaya: `vin-v/vin-m/vin-c/vin-p` (legacy, segment_count = 0) alongside `pli-tv-bu-vb/pli-tv-bi-vb/pli-tv-kd/pli-tv-pvr` (active, populated). - Abhidhamma: `ym/pt` (legacy = 0) alongside `ya/patthana` (active). - Use the `active` flag — each nikaya carries `active: true/false` (true ⇔ `segment_count > 0`). Pick `active` nikayas; the others are metadata placeholders from an older migration. 🌐 Languages: Returns Pāli + Thai + English labels regardless of enabled set (these are metadata, not segment text). Text content follows ENABLED_LANGUAGES. Thai translations aren't loaded yet.	Show the structure of all three pitakas with coverage statistics. 💡 Use this tool when: - The user asks for an overview of the Tipiṭaka (what's in it / which collections). - You need to check coverage before promising a search will find something — `segment_count > 0` is the active-loaded signal. - Verifying scope when compiling an artifact. 📊 Current state (v1.1+, at parity with SuttaCentral bilara-data): - Sutta Piṭaka complete: DN 37, MN 155, SN 1,829, AN 1,419, KN 2,351 sections (~284,702 segments) — Pāli + bilara EN (Sujato for DN/MN/SN/AN and most of KN; a few KN books have other bilara translators) - Vinaya Piṭaka complete: Bhikkhu Vibhaṅga 222, Bhikkhunī Vibhaṅga 127, Khandhaka 22, Parivāra 51 + Pātimokkha 2 (~71,557 segments) — Pāli + Brahmali EN - Abhidhamma Piṭaka complete: 7 books (ds, vb, dt, pp, kv, ya, patthana) ~88,414 segments — Pāli only (bilara has no English for any Abhidhamma book) - Total ~444,673 segments in the DB ⚠️ Known quirks: - The schema carries duplicate legacy + SC-modern codes side by side: - Vinaya: `vin-v/vin-m/vin-c/vin-p` (legacy, segment_count = 0) alongside `pli-tv-bu-vb/pli-tv-bi-vb/pli-tv-kd/pli-tv-pvr` (active, populated). - Abhidhamma: `ym/pt` (legacy = 0) alongside `ya/patthana` (active). - Use the `active` flag — each nikaya carries `active: true/false` (true ⇔ `segment_count > 0`). Pick `active` nikayas; the others are metadata placeholders from an older migration. 🌐 Languages: Returns Pāli + Thai + English labels regardless of enabled set (these are metadata, not segment text). Text content follows ENABLED_LANGUAGES. Thai translations aren't loaded yet.	No	Yes	Yes	No
`open_sutta_viewer`Open an interactive sutta viewer inside the chat — Pāli + English, plus an optional third row in the user's own language translated BY YOU. Renders each segment as: Pāli on top (canonical), the bilara-data English below it (verification anchor), and — when you supply `translations` — your translation in the user's language, clearly badged as AI-generated. Prefer this over dumping raw segments when the user wants to read a sutta. - `sutta_id` — standard SuttaCentral id, e.g. `sn56.11`, `mn10`, `dn22`. - `around` — a segment_id (e.g. `dn22:18.1`, from a search hit) to centre on; that segment is highlighted and scrolled into view. Use this after a search so the reader lands on the exact cited line. - `offset` — 0-based segment index for paging long suttas (use `next_offset` from the previous result). Do NOT combine with `around`. - `window` — segments before/after `around` to include (default 12). 🌐 Translating for the user (important): when the conversation language is neither English nor Pāli, you SHOULD translate the displayed segments and pass them via `translations` so the user reads in their own language while still seeing the originals: 1. Fetch the segments first (`get_sutta` with the same selector) so you have the exact Pāli + English text. (Already called this tool without translations? The result contains the segments — translate them and call this tool AGAIN with the same selector plus `translations` to upgrade the view.) Your translation must travel through the `translations` parameter to appear in the viewer — writing it as a normal chat message leaves the viewer bilingual and looks broken; the tool always accepts `translations`, so never report it as missing. 2. Translate from the Pāli as the source, using the English as a semantic guide — never relay-translate from English alone. Preserve untranslatable doctrinal terms (dukkha, jhāna, taṇhā…) as loanwords with a brief gloss instead of forcing equivalents	Open an interactive sutta viewer inside the chat — Pāli + English, plus an optional third row in the user's own language translated BY YOU. Renders each segment as: Pāli on top (canonical), the bilara-data English below it (verification anchor), and — when you supply `translations` — your translation in the user's language, clearly badged as AI-generated. Prefer this over dumping raw segments when the user wants to read a sutta. - `sutta_id` — standard SuttaCentral id, e.g. `sn56.11`, `mn10`, `dn22`. - `around` — a segment_id (e.g. `dn22:18.1`, from a search hit) to centre on; that segment is highlighted and scrolled into view. Use this after a search so the reader lands on the exact cited line. - `offset` — 0-based segment index for paging long suttas (use `next_offset` from the previous result). Do NOT combine with `around`. - `window` — segments before/after `around` to include (default 12). 🌐 Translating for the user (important): when the conversation language is neither English nor Pāli, you SHOULD translate the displayed segments and pass them via `translations` so the user reads in their own language while still seeing the originals: 1. Fetch the segments first (`get_sutta` with the same selector) so you have the exact Pāli + English text. (Already called this tool without translations? The result contains the segments — translate them and call this tool AGAIN with the same selector plus `translations` to upgrade the view.) Your translation must travel through the `translations` parameter to appear in the viewer — writing it as a normal chat message leaves the viewer bilingual and looks broken; the tool always accepts `translations`, so never report it as missing. 2. Translate from the Pāli as the source, using the English as a semantic guide — never relay-translate from English alone. Preserve untranslatable doctrinal terms (dukkha, jhāna, taṇhā…) as loanwords with a brief gloss instead of forcing equivalents	No	Yes	Yes	No
`parse_pali_word`Strip Pāli inflectional suffixes to find the root form (basic stem). 💡 Use this tool when: - You find an inflected Pāli word (e.g. `dukkhassa`, `bhikkhūnaṁ`) and `get_word_definition` doesn't find it directly — Pāli inflects nouns across 7 cases × 2 numbers, ~16 forms per root. - You want to see possible stems before another `get_word_definition` lookup. 🔄 Recommended workflow: `parse_pali_word(inflected_form)` → get `possible_stems[]` → call `get_word_definition(stem)` per stem until you find a definition. ⚠️ Limitations: - Rule-based first-pass — strips common suffixes (case endings, vowel shortening). Not a full morphological analyzer. - Compound words (samāsa) are NOT split — `dukkhanirodha` won't be broken into `dukkha` + `nirodha`. - Sandhi (sound junctions) like `tena ahaṁ → tenāhaṁ` aren't reversed. - Returns possible stems — verify each via `get_word_definition`. ⚠️ `possible_stems` are rule output, not findings. They come from the suffix table applied to your input — nothing was looked up, so no field carries a `segment_id`, and some candidates are not Pāli words at all. Treat each as a spelling to try in `get_word_definition`, never as an attested form.	Strip Pāli inflectional suffixes to find the root form (basic stem). 💡 Use this tool when: - You find an inflected Pāli word (e.g. `dukkhassa`, `bhikkhūnaṁ`) and `get_word_definition` doesn't find it directly — Pāli inflects nouns across 7 cases × 2 numbers, ~16 forms per root. - You want to see possible stems before another `get_word_definition` lookup. 🔄 Recommended workflow: `parse_pali_word(inflected_form)` → get `possible_stems[]` → call `get_word_definition(stem)` per stem until you find a definition. ⚠️ Limitations: - Rule-based first-pass — strips common suffixes (case endings, vowel shortening). Not a full morphological analyzer. - Compound words (samāsa) are NOT split — `dukkhanirodha` won't be broken into `dukkha` + `nirodha`. - Sandhi (sound junctions) like `tena ahaṁ → tenāhaṁ` aren't reversed. - Returns possible stems — verify each via `get_word_definition`. ⚠️ `possible_stems` are rule output, not findings. They come from the suffix table applied to your input — nothing was looked up, so no field carries a `segment_id`, and some candidates are not Pāli words at all. Treat each as a spelling to try in `get_word_definition`, never as an attested form.	No	Yes	Yes	No
`search_by_keyword`Keyword search across the Pāli Tipiṭaka (trigram word-similarity). Searches the configured enabled language(s) on the server. Filterable by pitaka and translation edition. 💡 Hints for the AI client: The system's canonical reference is Romanised Pāli (from SuttaCentral). If the user asks in a disabled or unsupported language, translate the keyword to Romanised Pāli (preferred) or English before calling this tool — e.g. "suffering" → "dukkha", "mindfulness of breathing" → "ānāpānassati". See the server instructions for the enabled language set. ✅ Diacritics do not matter. `anapanassati` and `ānāpānassati` return the same thing; so do `nibbana` and `nibbāna`. Write the macrons if you know them, guess without them if you don't — neither costs you results. ⚠️ A common Pāli noun is a poor query. `samudda` (sea) matches ~700 segments and the top of that list is mostly section headings, not the passage that teaches anything. Two things to do instead: - Search the rarest distinctive noun in the passage, not its most obvious one. For the simile of the blind turtle, `turtle`/`kacchapa` gets there; `ocean`/`samudda` does not, in either language. - Prefer English, or raise the limit. `turtle` returns SN 56.47 and SN 56.48 inside the default window; `kacchapa` matches them too but ranks them past 30, so you need `limit=50` to see them. - A word inside a compound may be out of reach entirely: `samudda` scores 0.50 against `mahāsamudde` (compounded and inflected), under the 0.6 cutoff, so SN 56.47 is not ranked low — it is excluded. Trying more spellings will not recover it; search a different word instead. 🔍 Pick the right search tool for the question shape: - Term lookup (exact word appearances) — e.g. "occurrences of `ānāpānassati`": this tool is best (trigram nails the exact word). - Concept search ("discourses about X") — e.g. "discourses about mindfulness of breathing": use `search_hybrid` instead. Canonic	Keyword search across the Pāli Tipiṭaka (trigram word-similarity). Searches the configured enabled language(s) on the server. Filterable by pitaka and translation edition. 💡 Hints for the AI client: The system's canonical reference is Romanised Pāli (from SuttaCentral). If the user asks in a disabled or unsupported language, translate the keyword to Romanised Pāli (preferred) or English before calling this tool — e.g. "suffering" → "dukkha", "mindfulness of breathing" → "ānāpānassati". See the server instructions for the enabled language set. ✅ Diacritics do not matter. `anapanassati` and `ānāpānassati` return the same thing; so do `nibbana` and `nibbāna`. Write the macrons if you know them, guess without them if you don't — neither costs you results. ⚠️ A common Pāli noun is a poor query. `samudda` (sea) matches ~700 segments and the top of that list is mostly section headings, not the passage that teaches anything. Two things to do instead: - Search the rarest distinctive noun in the passage, not its most obvious one. For the simile of the blind turtle, `turtle`/`kacchapa` gets there; `ocean`/`samudda` does not, in either language. - Prefer English, or raise the limit. `turtle` returns SN 56.47 and SN 56.48 inside the default window; `kacchapa` matches them too but ranks them past 30, so you need `limit=50` to see them. - A word inside a compound may be out of reach entirely: `samudda` scores 0.50 against `mahāsamudde` (compounded and inflected), under the 0.6 cutoff, so SN 56.47 is not ranked low — it is excluded. Trying more spellings will not recover it; search a different word instead. 🔍 Pick the right search tool for the question shape: - Term lookup (exact word appearances) — e.g. "occurrences of `ānāpānassati`": this tool is best (trigram nails the exact word). - Concept search ("discourses about X") — e.g. "discourses about mindfulness of breathing": use `search_hybrid` instead. Canonic	No	Yes	Yes	No
`search_hybrid`Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. This is the recommended tool for "discourses about X" / concept queries, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). ⚠️ Send the question and nothing else. Do not pad the query. The whole string becomes one vector, so every word you add moves it. Appending your own candidate terms — synonyms, Pāli equivalents, a keyword list — searches for the blend, not for the question. Measured on `Buddha flies in the sky`: asking it plainly put the right passage at rank 1 (3 relevant suttas). Appending three guessed Pāli terms (`buddha, agga, sagga`) pushed it down to rank 5 and left only 1 — because `agga` (supreme) and `sagga` (heaven) drag the vector toward their own meanings. Have candidate terms worth searching? Give them their own `search_by_keyword` call and merge the two result lists. One tool asks what a passage means, the other asks where a word occurs; combined into a single string they cancel out. Rewriting the question to sound more canonical does not help either — the same query phrased as `rose into the air and flew like a bird` scored zero relevant hits. 💡 Hints for the AI client: - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - `language` only chooses what comes back; it does not change what matches or how results are ranked. - Looking for a concrete thing rather than a concept (an animal, an	Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. This is the recommended tool for "discourses about X" / concept queries, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). ⚠️ Send the question and nothing else. Do not pad the query. The whole string becomes one vector, so every word you add moves it. Appending your own candidate terms — synonyms, Pāli equivalents, a keyword list — searches for the blend, not for the question. Measured on `Buddha flies in the sky`: asking it plainly put the right passage at rank 1 (3 relevant suttas). Appending three guessed Pāli terms (`buddha, agga, sagga`) pushed it down to rank 5 and left only 1 — because `agga` (supreme) and `sagga` (heaven) drag the vector toward their own meanings. Have candidate terms worth searching? Give them their own `search_by_keyword` call and merge the two result lists. One tool asks what a passage means, the other asks where a word occurs; combined into a single string they cancel out. Rewriting the question to sound more canonical does not help either — the same query phrased as `rose into the air and flew like a bird` scored zero relevant hits. 💡 Hints for the AI client: - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - `language` only chooses what comes back; it does not change what matches or how results are ranked. - Looking for a concrete thing rather than a concept (an animal, an	No	Yes	Yes	No
`search_semantic`Semantic search — match by meaning, not exact words. Uses vector similarity (cosine distance) over `text_pali` embedded with a multilingual MiniLM model. 🤔 In most cases you should use `search_hybrid` instead — it combines this semantic search with keyword search and ranks better. Use this tool only when you need: - Pure semantic results (no keyword influence) - Fine-grained `threshold` tuning (hybrid uses RRF which is harder to tune) - To debug what semantic alone picks up vs keyword ⚠️ Known limitations: - The index is Pāli only (English/Thai queries pass through the multilingual embedding but the model isn't tuned on Pāli) - English queries usually embed better than Thai (model is EN-primary) - For specific Pāli terms (`appamāda`, `dukkha`), exact match is better — use `search_by_keyword` instead - Pāli stock phrases recur in many suttas → similarity scores cluster; read the top 10, don't trust rank 1 alone 📐 Reading `distance`. It is cosine distance between your query's embedding and the segment's, so smaller is closer, and a row is returned only at or under `threshold` (default 0.7) — an empty result means nothing cleared that cut. Ordering is proximity of meaning in one model's space, not doctrinal importance.	Semantic search — match by meaning, not exact words. Uses vector similarity (cosine distance) over `text_pali` embedded with a multilingual MiniLM model. 🤔 In most cases you should use `search_hybrid` instead — it combines this semantic search with keyword search and ranks better. Use this tool only when you need: - Pure semantic results (no keyword influence) - Fine-grained `threshold` tuning (hybrid uses RRF which is harder to tune) - To debug what semantic alone picks up vs keyword ⚠️ Known limitations: - The index is Pāli only (English/Thai queries pass through the multilingual embedding but the model isn't tuned on Pāli) - English queries usually embed better than Thai (model is EN-primary) - For specific Pāli terms (`appamāda`, `dukkha`), exact match is better — use `search_by_keyword` instead - Pāli stock phrases recur in many suttas → similarity scores cluster; read the top 10, don't trust rank 1 alone 📐 Reading `distance`. It is cosine distance between your query's embedding and the segment's, so smaller is closer, and a row is returned only at or under `threshold` (default 0.7) — an empty result means nothing cleared that cut. Ordering is proximity of meaning in one model's space, not doctrinal importance.	No	Yes	Yes	No
`survey_corpus`Exhaustively survey the WHOLE Tipiṭaka for a term — guaranteed complete. Use this (not `search_by_keyword`) when the question is about coverage or counting rather than "show me the best passages": - "How many times does Kusinārā appear in the canon?" - "Every place ānāpānassati is mentioned — don't miss any" - "Which pitakas/how many suttas mention this term?" Unlike `search_by_keyword` (ranked, capped at 50, no total), this returns an exact count, a per-pitaka breakdown, the distinct surface forms that matched (so you can audit and discard over-matches), and a paginated enumeration. The `lexical` result carries `complete: true` — a hard guarantee that nothing was dropped for the chosen `match_scope`. Two layers, two different promises: - lexical — the word and its forms. Deterministic + EXHAUSTIVE. - semantic (`mode="thorough"`, hosted only) — passages teaching the same concept with DIFFERENT vocabulary (e.g. ānāpānassati via `assasati`/`passasati`). Approximate, NOT exhaustive — it never claims completeness, it only boosts recall.	Exhaustively survey the WHOLE Tipiṭaka for a term — guaranteed complete. Use this (not `search_by_keyword`) when the question is about coverage or counting rather than "show me the best passages": - "How many times does Kusinārā appear in the canon?" - "Every place ānāpānassati is mentioned — don't miss any" - "Which pitakas/how many suttas mention this term?" Unlike `search_by_keyword` (ranked, capped at 50, no total), this returns an exact count, a per-pitaka breakdown, the distinct surface forms that matched (so you can audit and discard over-matches), and a paginated enumeration. The `lexical` result carries `complete: true` — a hard guarantee that nothing was dropped for the chosen `match_scope`. Two layers, two different promises: - lexical — the word and its forms. Deterministic + EXHAUSTIVE. - semantic (`mode="thorough"`, hosted only) — passages teaching the same concept with DIFFERENT vocabulary (e.g. ānāpānassati via `assasati`/`passasati`). Approximate, NOT exhaustive — it never claims completeness, it only boosts recall.	No	Yes	Yes	No
`verify_quote`Verify, check, or confirm a Buddha quote — does the canon really say it? Fact-check a quotation attributed to the Buddha. Catches fake, misquoted, misattributed and misremembered passages, and finds the true reading. Paste a line that has been quoted or half-remembered — Pāli or English — and this says whether the canon really contains it, cites where, and if not, shows the closest thing that is actually there. 🧭 When to use this: - A quote is attributed to the Buddha and you are not certain it is real. A fabricated line that sounds canonical is the hardest error to catch by reading, because it reads correctly. Check it instead of trusting it. - Someone recalls a passage imperfectly, or a chanted form has drifted from the written one. The tool shows the received text beside theirs. - Before repeating any Pāli you did not get from these tools, verify it. This is cheap and it is the difference between citing and guessing. ⚠️ Do not present an unverified passage as canonical. If the verdict is `not_found`, say plainly that the line is not in the canon rather than quoting it with a hedge — a hedged fabrication still spreads. ⚖️ Verdict thresholds. `exact` = `contained` or `similarity` ≥ 0.995; `close` = `coverage` ≥ 0.85 and `similarity` ≥ 0.55; else `not_found`. `exact` means letters matched once folded, not that a human confirmed the reading. Matches also carry `coverage` (your letters present, in order), `contained` (present whole) and `spans` (segments joined; >1 → `segment_id` is the first only).	Verify, check, or confirm a Buddha quote — does the canon really say it? Fact-check a quotation attributed to the Buddha. Catches fake, misquoted, misattributed and misremembered passages, and finds the true reading. Paste a line that has been quoted or half-remembered — Pāli or English — and this says whether the canon really contains it, cites where, and if not, shows the closest thing that is actually there. 🧭 When to use this: - A quote is attributed to the Buddha and you are not certain it is real. A fabricated line that sounds canonical is the hardest error to catch by reading, because it reads correctly. Check it instead of trusting it. - Someone recalls a passage imperfectly, or a chanted form has drifted from the written one. The tool shows the received text beside theirs. - Before repeating any Pāli you did not get from these tools, verify it. This is cheap and it is the difference between citing and guessing. ⚠️ Do not present an unverified passage as canonical. If the verdict is `not_found`, say plainly that the line is not in the canon rather than quoting it with a hedge — a hedged fabrication still spreads. ⚖️ Verdict thresholds. `exact` = `contained` or `similarity` ≥ 0.995; `close` = `coverage` ≥ 0.85 and `similarity` ≥ 0.55; else `not_found`. `exact` means letters matched once folded, not that a human confirmed the reading. Matches also carry `coverage` (your letters present, in order), `contained` (present whole) and `spans` (segments joined; >1 → `segment_id` is the first only).	No	Yes	Yes	No

Details

Registry status: Active
Published: May 12, 2026
Updated: May 12, 2026
Website: https://tripitaka-mcp.com/⁠ (opens in a new tab)
Repository: https://github.com/dhamma-seeker/tripitaka-mcp⁠ (opens in a new tab)
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