이 MCP로 할 수 있는 일
Indexes AI inference models and providers, including current and historical pricing, provider comparisons, quality-adjusted metrics, privacy attributes, and model recommendations.
기능
도구
10
도구
compare_providers
Compare the price of one model across the providers that host it.
Args:
model_id: Canonical model id, e.g. 'meta/muse-spark-1.1'.
Returns: per-provider endpoints with pricing, showing where direct
provider prices diverge (e.g. from OpenRouter's negotiated rate).
입력 스키마
{'type': 'object', 'title': 'compare_providersArguments', 'required': ['model_id'], 'properties': {'model_id': {'type': 'string', 'title': 'Model Id'}}}
출력 스키마
{'type': 'object', 'title': 'compare_providersDictOutput', 'additionalProperties': True}
explain_model
Get everything about one model in a single call: the full picture.
Returns current pricing (input/output/blended, Cost/IQ, 24h/7d changes),
a price-history summary with trend, all provider endpoints, the cheapest
hand-verified endpoint with its native model id (for hot-swapping),
privacy flags (ZDR/EU availability), and the AA intelligence score.
Everything is as-of stamped.
Args:
model_id: Canonical model id, e.g. 'anthropic/claude-sonnet-5'.
history_days: Price-history window (default 30, max 365).
Returns: complete model profile with pricing, endpoints, privacy, quality.
입력 스키마
{'type': 'object', 'title': 'explain_modelArguments', 'required': ['model_id'], 'properties': {'model_id': {'type': 'string', 'title': 'Model Id'}, 'history_days': {'type': 'integer', 'title': 'History Days', 'default': 30}}}
출력 스키마
{'type': 'object', 'title': 'explain_modelDictOutput', 'additionalProperties': True}
get_composite_history
Get SIT-Composite index history / trend over time.
Args:
days: History window in days (1-90, default 30).
Returns: historical composite index values.
입력 스키마
{'type': 'object', 'title': 'get_composite_historyArguments', 'properties': {'days': {'type': 'integer', 'title': 'Days', 'default': 30}}}
출력 스키마
{'type': 'object', 'title': 'get_composite_historyDictOutput', 'additionalProperties': True}
get_composite_latest
Get the current SIT-Composite index value + per-tier breakdown.
The SIT-Composite is a usage-weighted mean of the top-50 models by token
volume, reflecting what developers actually pay for inference.
입력 스키마
{'type': 'object', 'title': 'get_composite_latestArguments', 'properties': {}}
출력 스키마
{'type': 'object', 'title': 'get_composite_latestDictOutput', 'additionalProperties': True}
get_model
Get full detail + current pricing for one model by its id.
Args:
model_id: Canonical model id, e.g. 'openai/gpt-5.6' or 'anthropic/claude-sonnet-5'.
Returns: pricing, tier, SIT score, quality-adjusted price (Cost/IQ).
입력 스키마
{'type': 'object', 'title': 'get_modelArguments', 'required': ['model_id'], 'properties': {'model_id': {'type': 'string', 'title': 'Model Id'}}}
출력 스키마
{'type': 'object', 'title': 'get_modelDictOutput', 'additionalProperties': True}
get_model_history
Get HISTORICAL price data / trends for one model.
This is InferenceIndexer's differentiator: aggregators like OpenRouter
expose only current price; this returns the price over time (input,
output, blended $/M), enabling trend analysis.
Args:
model_id: Canonical model id, e.g. 'openai/gpt-5.6'.
days: History window in days (1-365, default 30; plan-dependent).
Returns: historical price series for the model.
입력 스키마
{'type': 'object', 'title': 'get_model_historyArguments', 'required': ['model_id'], 'properties': {'days': {'type': 'integer', 'title': 'Days', 'default': 30}, 'model_id': {'type': 'string', 'title': 'Model Id'}}}
출력 스키마
{'type': 'object', 'title': 'get_model_historyDictOutput', 'additionalProperties': True}
get_provider
Get detail for one provider: models, tier breakdown, price range.
Args:
provider_name: Provider name, e.g. 'DeepInfra', 'Novita', 'Venice'.
Returns: provider detail with model list and pricing.
입력 스키마
{'type': 'object', 'title': 'get_providerArguments', 'required': ['provider_name'], 'properties': {'provider_name': {'type': 'string', 'title': 'Provider Name'}}}
출력 스키마
{'type': 'object', 'title': 'get_providerDictOutput', 'additionalProperties': True}
list_providers
List all inference providers with model counts and price stats.
입력 스키마
{'type': 'object', 'title': 'list_providersArguments', 'properties': {}}
출력 스키마
{'type': 'object', 'title': 'list_providersDictOutput', 'additionalProperties': True}
recommend_models
Recommend the best-value AI models for given constraints, ranked with receipts.
The core answer endpoint: give it constraints and it returns the top models
ranked by Cost/IQ (quality-adjusted price, lower is better), each with a
plain-English 'why', a hot-swap endpoint_config (provider base_url + native
model id, ready to call), as-of timestamps, and runner-ups.
Args:
budget_max_usd_per_m: Max blended price $/M (optional).
context_min: Minimum context window in tokens (optional).
modality: 'text' (default), 'vision', or 'any'.
zdr: Require zero-data-retention providers (optional).
eu_sovereign: Require EU-sovereign providers (optional).
reasoning: Filter reasoning models (null = any, true/false).
limit: Max recommendations (1-20, default 5).
Returns: ranked recommendations with endpoint_config and ranking evidence.
입력 스키마
{'type': 'object', 'title': 'recommend_modelsArguments', 'properties': {'zdr': {'type': 'boolean', 'title': 'Zdr', 'default': False}, 'limit': {'type': 'integer', 'title': 'Limit', 'default': 5}, 'modality': {'type': 'string', 'title': 'Modality', 'default': 'text'}, 'reasoning': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'title': 'Reasoning', 'default': None}, 'context_min': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'Context Min', 'default': None}, 'eu_sovereign': {'type': 'boolean', 'title': 'Eu Sovereign', 'default': False}, 'budget_max_usd_per_m': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Budget Max Usd Per M', 'default': None}}}
출력 스키마
{'type': 'object', 'title': 'recommend_modelsDictOutput', 'additionalProperties': True}
search_models
Search and list AI inference models with current pricing.
Args:
query: Text search on model id/name (optional).
tier: Filter by tier: frontier | standard | budget | micro | zdr | eu (optional).
limit: Max results (1-100, default 25).
sort: Sort key, e.g. 'blended' (price), 'sit' (SIT score) (optional).
Returns: models with input/output/blended $/M pricing, provider, tier.
입력 스키마
{'type': 'object', 'title': 'search_modelsArguments', 'properties': {'sort': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Sort', 'default': None}, 'tier': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Tier', 'default': None}, 'limit': {'type': 'integer', 'title': 'Limit', 'default': 25}, 'query': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Query', 'default': None}}}
출력 스키마
{'type': 'object', 'title': 'search_modelsDictOutput', 'additionalProperties': True}
기록
최근 도구 변경
추가됨
compare_providers
2026년 9월 17일 12:41 PM
추가됨
get_composite_history
2026년 9월 17일 12:41 PM
추가됨
get_composite_latest
2026년 9월 17일 12:41 PM
추가됨
get_provider
2026년 9월 17일 12:41 PM
추가됨
list_providers
2026년 9월 17일 12:41 PM
추가됨
get_model_history
2026년 9월 17일 12:41 PM
추가됨
get_model
2026년 9월 17일 12:41 PM
추가됨
search_models
2026년 9월 17일 12:41 PM
추가됨
explain_model
2026년 9월 17일 12:41 PM
추가됨
recommend_models
2026년 9월 17일 12:41 PM
탐색
유사한 MCP 서버
Huggingface
io.github.pipeworx-io/huggingface
Provides access to Hugging Face model, dataset, and Space metadata, alongside broader structured research and data-routing tools.
Ai Model Experiments
io.github.pipeworx-io/ai-model-experiments
Runs prompts across multiple AI models and compares their outputs, costs, latency, token usage, and errors.
branchly
io.branchly/branchly
Manages an AI application’s knowledge-base content, prompts, tools, data sources, retrieval context, sessions, and usage analytic…
myriade
ai.myriade/myriade
Lets users explore and query a data warehouse through an AI data analyst agent.
HuggingFace New Dataset Release Tracker (hfdatasets)
com.a2awire/data-new-dataset-release-huggingface-tracker
Tracks newly released Hugging Face datasets, including updates, tags, and licenses.
aifu Agent Market
asia.aifu/agent-market
Discovers and hires agents or approved humans through a job ledger, verifies agent performance, and provides crypto, equity, sect…
SigRank — AI Operator Benchmarking
com.signalaf/sigrank
Benchmarks AI operator token usage, calculates cascade metrics, compares leaderboard performance, diagnoses efficiency, and simul…
Automan
io.github.i02202/automan
Offers paid AI skills for data analysis, classification, extraction, reporting, summarization, translation, text editing, copywri…