Etuki FinOps & LLM Pricing Oracle

io.github.cooyun/etuki-finops-mcpVersion 40.7.4

Cryptographic 563-leaf LLM rate oracle, context-cliff auditor, and prompt cache optimizer.

Status

Responds280 msNov 14, 2026, 14:48 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 14:48 UTC
Latency: 280 ms
Remotes responding: 1 of 1
Sign-in: No sign-in
Hosting: Hosted outside the EU (detected)
Registry data as of: Nov 14, 2026, 22:14 UTC

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Connect

Streamable HTTP

https://www.etuki.com/api/v1/mcpResponds280 msNov 14, 2026, 14:48 UTC

No sign-in

Claude Code

claude mcp add --transport http etuki-finops-mcp https://www.etuki.com/api/v1/mcp

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: etuki-finops-mcp
Remote MCP server URL: https://www.etuki.com/api/v1/mcp

Cursor

{
  "mcpServers": {
    "etuki-finops-mcp": {
      "url": "https://www.etuki.com/api/v1/mcp"
    }
  }
}

VS Code

code --add-mcp '{"name":"etuki-finops-mcp","type":"http","url":"https://www.etuki.com/api/v1/mcp"}'

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Tools

9 tools

Tools of Etuki FinOps & LLM Pricing Oracle
	Description		Read-only	Idempotent	Open world
`auto_route_task_by_quality_tier`Simulates a 6-turn coding workflow cost projection comparing unrouted flagship execution against tiered routing across grant pools and warm-cached GA models. Stateless in-memory calculation requiring no authentication and mutating no router settings. Scales per-turn cost linearly with context_tokens relative to current_model; when has_custom_router_config is true, reports a non-intrusive shadow comparison rather than active target IDs. Returns JSON with custom_router_mutex_check, six_turn_cost_breakdown (1 cold write turn + 5 warm read turns), and net_saved_usd (positive USD saved vs unrouted baseline). Use this tool when planning multi-turn task allocation; use check_context_cliff for single-turn length checks or reconcile_usage_receipt for post-hoc invoice auditing.	Simulates a 6-turn coding workflow cost projection comparing unrouted flagship execution against tiered routing across grant pools and warm-cached GA models. Stateless in-memory calculation requiring no authentication and mutating no router settings. Scales per-turn cost linearly with context_tokens relative to current_model; when has_custom_router_config is true, reports a non-intrusive shadow comparison rather than active target IDs. Returns JSON with custom_router_mutex_check, six_turn_cost_breakdown (1 cold write turn + 5 warm read turns), and net_saved_usd (positive USD saved vs unrouted baseline). Use this tool when planning multi-turn task allocation; use check_context_cliff for single-turn length checks or reconcile_usage_receipt for post-hoc invoice auditing.	No	Yes	Yes	No
`check_context_cliff`Evaluates whether a prompt token count crosses vendor long-context surcharge thresholds (100K on Claude Haiku 5.5, 128K on GPT-4o, 200K on Gemini 2.5 Pro, or 272K on GPT-6.1 Sol) and returns the surcharge multiplier and recommended flat-context alternative. Deterministic threshold lookup against the embedded 563-entity UMCE registry requiring no API keys. Returns JSON with crosses_cliff_boolean, active_surcharge_multiplier (1.0x below cliff vs 2.0x-5.0x above cliff), and recommended_flat_context_model. Compares prompt_tokens directly against model's specific cliff boundary in the UMCE catalog. Use this tool before sending large prompts (>90K tokens) to avoid 2x-5x cliff surcharges; use compress_and_fold_context instead to shrink the prompt below the cliff.	Evaluates whether a prompt token count crosses vendor long-context surcharge thresholds (100K on Claude Haiku 5.5, 128K on GPT-4o, 200K on Gemini 2.5 Pro, or 272K on GPT-6.1 Sol) and returns the surcharge multiplier and recommended flat-context alternative. Deterministic threshold lookup against the embedded 563-entity UMCE registry requiring no API keys. Returns JSON with crosses_cliff_boolean, active_surcharge_multiplier (1.0x below cliff vs 2.0x-5.0x above cliff), and recommended_flat_context_model. Compares prompt_tokens directly against model's specific cliff boundary in the UMCE catalog. Use this tool before sending large prompts (>90K tokens) to avoid 2x-5x cliff surcharges; use compress_and_fold_context instead to shrink the prompt below the cliff.	No	Yes	Yes	No
`compress_and_fold_context`Compresses verbose terminal or build logs by folding repetitive middle progress lines while preserving 100% of head lines, tail lines, compiler errors, stack traces, and file paths. Deterministic in-memory string transformation that mutates no files and requires no authentication. Returns JSON containing line_accounting_reconciliation (where raw_total_lines equals kept_original_lines_total plus omitted_repetitive_lines), byte_and_token_reconciliation (pricing saved tokens against model's input rate), and folded_context_payload. When raw_text is empty or under 50 lines, returns the diagnostic benchmark or unmodified input without truncation. Use this tool when build or test output exceeds 50 lines before appending to agent context; use pin_single_model_prefix_cache instead when optimizing static system prompt prefixes.	Compresses verbose terminal or build logs by folding repetitive middle progress lines while preserving 100% of head lines, tail lines, compiler errors, stack traces, and file paths. Deterministic in-memory string transformation that mutates no files and requires no authentication. Returns JSON containing line_accounting_reconciliation (where raw_total_lines equals kept_original_lines_total plus omitted_repetitive_lines), byte_and_token_reconciliation (pricing saved tokens against model's input rate), and folded_context_payload. When raw_text is empty or under 50 lines, returns the diagnostic benchmark or unmodified input without truncation. Use this tool when build or test output exceeds 50 lines before appending to agent context; use pin_single_model_prefix_cache instead when optimizing static system prompt prefixes.	No	Yes	Yes	No
`detect_model_downgrade_and_substitution`Evaluates LLM rate-card tiers to quantify relay downgrade risk, cost ratios, and remaining coding session turns. Deterministic in-memory calculation requiring no API keys; unknown model IDs safely fall back to the flagship baseline without error. Sums burned_usd and remaining_usd into the total session pool and projects remaining turns against a $137.45 reference budget, returning cost ratios, an endurance matrix, and a recommended production model ID. Use this tool when comparing session burn rates or suspected relay throttling; use reconcile_usage_receipt for single invoice lines, check_context_cliff for prompt-length surcharges, or optimize_prompt_cache_layout for mid-prompt cache breakpoint fixes.	Evaluates LLM rate-card tiers to quantify relay downgrade risk, cost ratios, and remaining coding session turns. Deterministic in-memory calculation requiring no API keys; unknown model IDs safely fall back to the flagship baseline without error. Sums burned_usd and remaining_usd into the total session pool and projects remaining turns against a $137.45 reference budget, returning cost ratios, an endurance matrix, and a recommended production model ID. Use this tool when comparing session burn rates or suspected relay throttling; use reconcile_usage_receipt for single invoice lines, check_context_cliff for prompt-length surcharges, or optimize_prompt_cache_layout for mid-prompt cache breakpoint fixes.	No	Yes	Yes	No
`discover_and_probe_account_models`Retrieves verified production GA models and eligible zero-cost grant tiers (Google Cloud Tier 1, AWS Activate Bedrock, OpenRouter :free) from the embedded UMCE registry. Deterministic local snapshot lookup that issues zero network probes, requires no API keys, and consumes zero user quota. Returns JSON listing zero-cost workhorse models, flagship reasoning models, and recommended role assignments benchmarked against current_model (formatted as 'vendor/model-slug', defaulting to 'openai/gpt-6.1-sol'). Use this tool at session startup to inspect available model tiers before calling auto_route_task_by_quality_tier; use list_supported_models instead when filtering the full 563-entity Merkle registry by vendor.	Retrieves verified production GA models and eligible zero-cost grant tiers (Google Cloud Tier 1, AWS Activate Bedrock, OpenRouter :free) from the embedded UMCE registry. Deterministic local snapshot lookup that issues zero network probes, requires no API keys, and consumes zero user quota. Returns JSON listing zero-cost workhorse models, flagship reasoning models, and recommended role assignments benchmarked against current_model (formatted as 'vendor/model-slug', defaulting to 'openai/gpt-6.1-sol'). Use this tool at session startup to inspect available model tiers before calling auto_route_task_by_quality_tier; use list_supported_models instead when filtering the full 563-entity Merkle registry by vendor.	No	Yes	Yes	No
`list_supported_models`Queries the embedded UMCE rate-card registry by vendor namespace to retrieve canonical model IDs, input/output/cache USD prices per 1M tokens, context windows, and Merkle leaf hashes. Deterministic local lookup requiring no API keys; unknown vendor slugs safely return total_matched_entities: 0 and an empty models array without error. Passing provider='all' returns the multi-vendor catalog summary, while a specific vendor slug filters strictly by namespace prefix. Use this tool when filtering the full rate-card catalog by vendor; use discover_and_probe_account_models instead at session startup to select zero-cost grant pools and flagship routing tiers.	Queries the embedded UMCE rate-card registry by vendor namespace to retrieve canonical model IDs, input/output/cache USD prices per 1M tokens, context windows, and Merkle leaf hashes. Deterministic local lookup requiring no API keys; unknown vendor slugs safely return total_matched_entities: 0 and an empty models array without error. Passing provider='all' returns the multi-vendor catalog summary, while a specific vendor slug filters strictly by namespace prefix. Use this tool when filtering the full rate-card catalog by vendor; use discover_and_probe_account_models instead at session startup to select zero-cost grant pools and flagship routing tiers.	No	Yes	Yes	No
`optimize_prompt_cache_layout`Reorders a single prompt's dynamic mid-turn tokens after static prefix tokens to prevent 1.25x cold-cache invalidation and restore 0.1x warm cache reads. Advisory in-memory simulation that mutates no live prompts and requires no authentication; zero-token inputs return 0 USD cost without error. Sums static_prefix_tokens and dynamic_mid_tokens to compare before_layout_cost_usd against after_layout_cost_usd and net_savings_pct (0-100). Use this tool when diagnosing mid-prompt timestamps or tool outputs that break cache breakpoints; use pin_single_model_prefix_cache instead to project 10-turn session economics for a target model ID.	Reorders a single prompt's dynamic mid-turn tokens after static prefix tokens to prevent 1.25x cold-cache invalidation and restore 0.1x warm cache reads. Advisory in-memory simulation that mutates no live prompts and requires no authentication; zero-token inputs return 0 USD cost without error. Sums static_prefix_tokens and dynamic_mid_tokens to compare before_layout_cost_usd against after_layout_cost_usd and net_savings_pct (0-100). Use this tool when diagnosing mid-prompt timestamps or tool outputs that break cache breakpoints; use pin_single_model_prefix_cache instead to project 10-turn session economics for a target model ID.	No	Yes	Yes	No
`pin_single_model_prefix_cache`Projects 10-turn session caching costs for a target model under Case A (100% static prefix) and Case B (90% static prefix + 10% dynamic tail) and outputs provider cache-control templates. In-memory actuarial lookup requiring no API keys; unknown model IDs default to 'openai/gpt-6.1-sol' and prompts under 1,024 tokens bypass warm discounts. Resolves model's warm multiplier (0.05x-0.25x) from the UMCE registry to return provider_cache_protocol_mechanism, ten_turn_cost_comparison_on_same_model, and two_vector_actuarial_reference_table (benchmarking 150K and 200K token reference vectors). Use this tool to quantify 10-turn caching ROI on a specific model; use optimize_prompt_cache_layout instead when fixing mid-prompt token ordering.	Projects 10-turn session caching costs for a target model under Case A (100% static prefix) and Case B (90% static prefix + 10% dynamic tail) and outputs provider cache-control templates. In-memory actuarial lookup requiring no API keys; unknown model IDs default to 'openai/gpt-6.1-sol' and prompts under 1,024 tokens bypass warm discounts. Resolves model's warm multiplier (0.05x-0.25x) from the UMCE registry to return provider_cache_protocol_mechanism, ten_turn_cost_comparison_on_same_model, and two_vector_actuarial_reference_table (benchmarking 150K and 200K token reference vectors). Use this tool to quantify 10-turn caching ROI on a specific model; use optimize_prompt_cache_layout instead when fixing mid-prompt token ordering.	No	Yes	Yes	No
`reconcile_usage_receipt`Audits a single API usage receipt against official 1x manufacturer rate cards from the 563-entity UMCE registry to quantify relay multiplier markups and unutilized prefix cache waste. Deterministic mathematical audit in memory requiring no authentication. Returns JSON with expected_theoretical_cost_usd, billing_variance_usd, variance_status ('OVERCHARGED_BY_RELAY_OR_MARKUP' when variance > $0.001 else 'EXACT_RATE_CARD_MATCH'), same_model_unutilized_cache_waste_usd, and net_recoverable_savings_usd. Enforces cached_tokens <= prompt_tokens, pricing (prompt_tokens - cached_tokens) at uncached input rates and cached_tokens at warm cache rates before comparing against actual_billed_usd. Use this tool when verifying an invoice line item; use detect_model_downgrade_and_substitution instead for session-level budget endurance projections.	Audits a single API usage receipt against official 1x manufacturer rate cards from the 563-entity UMCE registry to quantify relay multiplier markups and unutilized prefix cache waste. Deterministic mathematical audit in memory requiring no authentication. Returns JSON with expected_theoretical_cost_usd, billing_variance_usd, variance_status ('OVERCHARGED_BY_RELAY_OR_MARKUP' when variance > $0.001 else 'EXACT_RATE_CARD_MATCH'), same_model_unutilized_cache_waste_usd, and net_recoverable_savings_usd. Enforces cached_tokens <= prompt_tokens, pricing (prompt_tokens - cached_tokens) at uncached input rates and cached_tokens at warm cache rates before comparing against actual_billed_usd. Use this tool when verifying an invoice line item; use detect_model_downgrade_and_substitution instead for session-level budget endurance projections.	No	Yes	Yes	No

Details

Registry status: Active
Published: Nov 1, 2026
Updated: Nov 1, 2026
Website: Not given
Repository: https://github.com/cooyun/etuki-gate⁠ (opens in a new tab)
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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