ContexteTech

io.github.contexte-tech/contexteVersion 0.5.2

Search and load AI datasets, prompts and few-shot contexts from the ContexteTech catalogue.

Status

Responds174 msNov 14, 2026, 14:48 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 14:48 UTC
Latency: 174 ms
Remotes responding: 1 of 1
Sign-in: No sign-in
Hosting: EU hosting detected (detected, not a legal statement)
Registry data as of: Nov 14, 2026, 21:12 UTC

Data from the official MCP Registry, checked by run.zero. How we check

Connect

Streamable HTTP

https://contextetech.com/mcpResponds174 msNov 14, 2026, 14:48 UTC

No sign-in

Claude Code

claude mcp add --transport http contexte https://contextetech.com/mcp

Claude Desktop and claude.ai

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

Cursor

{
  "mcpServers": {
    "contexte": {
      "url": "https://contextetech.com/mcp"
    }
  }
}

VS Code

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

Streamable HTTP

pypi: contexte 0.5.1

Package only

PyPI (uvx)

claude mcp add contexte -- uvx contexte==0.5.1

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Tools

6 tools

Tools of ContexteTech
	Description		Read-only	Idempotent	Open world
`estimate_cost`Estimate in euros what a ContexteTech resource costs to run with common AI models, from the week's list prices. Use it when the user asks about cost, budget or the cheapest model. For a prompt or context: input and output tokens, cost per call for each model and batch discount; for a dataset: cost of one pass over all rows and a fine-tuning estimate on rented GPUs (hours and offers); for a model: memory (VRAM) needed and GPU offers that fit. Returns available: false with a note when no estimate exists for that type.	Estimate in euros what a ContexteTech resource costs to run with common AI models, from the week's list prices. Use it when the user asks about cost, budget or the cheapest model. For a prompt or context: input and output tokens, cost per call for each model and batch discount; for a dataset: cost of one pass over all rows and a fine-tuning estimate on rented GPUs (hours and offers); for a model: memory (VRAM) needed and GPU offers that fit. Returns available: false with a note when no estimate exists for that type.	No	Yes	Not declared	Yes
`fetch`Fetch the full content of a ContexteTech resource by the id returned by search.	Fetch the full content of a ContexteTech resource by the id returned by search.	No	Yes	Not declared	Yes
`fill_prompt`Fill a ContexteTech prompt template and return the ready-to-send text, nothing else. Use it when the user wants a prompt applied to their own case; call load_resource first if you need the template or the exact variable names. 'variables' maps each {{name}} of the template to its text value; extra keys are ignored. Fails with an error if the code is unknown, if the resource is not a prompt, or if a template variable is missing (the error lists the missing names).	Fill a ContexteTech prompt template and return the ready-to-send text, nothing else. Use it when the user wants a prompt applied to their own case; call load_resource first if you need the template or the exact variable names. 'variables' maps each {{name}} of the template to its text value; extra keys are ignored. Fails with an error if the code is unknown, if the resource is not a prompt, or if a template variable is missing (the error lists the missing names).	No	Yes	Not declared	Yes
`load_resource`Read one ContexteTech resource, usually right after search_resources or when the user gives a code or page URL. Accepts the short code (end of the page URL, e.g. 'jr6t0n'), 'author/name', or 'code#v2' for a fixed version. Always returns title, type, licence, language, source, version and page URL; plus, for a dataset, the total number of rows, the first rows (default 10, max 50); for a prompt, the template and its {{variables}} (then call fill_prompt); for a context, the system prompt, knowledge and examples. The result ends with a Source line (resource, author, original data source, publisher, licence): citing it with its links is required. Fails with an error if the code does not exist or the resource is private.	Read one ContexteTech resource, usually right after search_resources or when the user gives a code or page URL. Accepts the short code (end of the page URL, e.g. 'jr6t0n'), 'author/name', or 'code#v2' for a fixed version. Always returns title, type, licence, language, source, version and page URL; plus, for a dataset, the total number of rows, the first rows (default 10, max 50); for a prompt, the template and its {{variables}} (then call fill_prompt); for a context, the system prompt, knowledge and examples. The result ends with a Source line (resource, author, original data source, publisher, licence): citing it with its links is required. Fails with an error if the code does not exist or the resource is private.	No	Yes	Not declared	Yes
`search`Search the ContexteTech catalogue of AI datasets, prompts and contexts. Returns results with id, title and url.	Search the ContexteTech catalogue of AI datasets, prompts and contexts. Returns results with id, title and url.	No	Yes	Not declared	Yes
`search_resources`Search the ContexteTech catalogue of AI building blocks: fine-tuning and evaluation datasets (chat, DPO, agent traces), prompts, few-shot contexts, MCP servers, models and more, many grounded in official European sources, in 9 languages. Use it first when the user needs AI training or evaluation data, a prompt, a context or an AI component, then call load_resource with a returned code. Write the topic in a few words, in English or the content language; common topics are matched across French, English, German and Spanish (e.g. 'marine weather' finds 'météo marine'), and a type word such as 'dataset' or 'prompt' puts that type first. Resources matching every topic come first; weaker matches carry match: partial. Returns up to 15 matches, each with short code, title, type, language, licence, author, rows (datasets), description and page URL; when nothing matches, returns a short message suggesting how to rephrase.	Search the ContexteTech catalogue of AI building blocks: fine-tuning and evaluation datasets (chat, DPO, agent traces), prompts, few-shot contexts, MCP servers, models and more, many grounded in official European sources, in 9 languages. Use it first when the user needs AI training or evaluation data, a prompt, a context or an AI component, then call load_resource with a returned code. Write the topic in a few words, in English or the content language; common topics are matched across French, English, German and Spanish (e.g. 'marine weather' finds 'météo marine'), and a type word such as 'dataset' or 'prompt' puts that type first. Resources matching every topic come first; weaker matches carry match: partial. Returns up to 15 matches, each with short code, title, type, language, licence, author, rows (datasets), description and page URL; when nothing matches, returns a short message suggesting how to rephrase.	No	Yes	Not declared	Yes

Details

Registry status: Active
Published: Oct 15, 2026
Updated: Oct 15, 2026
Website: https://contextetech.com/en/⁠ (opens in a new tab)
Repository: Not given
Categories (suggested): AI modelsSuggested categories are assigned automatically from the registry text and may be wrong. Verified operators can correct them.
GDPR: Not declared

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