inferenceindexer-mcp
io.github.DesMartin01/inferenceindexer-mcpVersion 0.1.1AI inference pricing for agents: live and historical model prices, provider comparison.
Status
Responds161 msAnswered our handshake without sign-in.- Last checked
- Nov 14, 2026, 14:37 UTC
- Latency
- 161 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, 22:14 UTC
Data from the official MCP Registry, checked by run.zero. How we check
Connect
Streamable HTTP
https://api.inferenceindexer.ai/mcpResponds161 msNo sign-in
Claude Code
claude mcp add --transport http inferenceindexer-mcp https://api.inferenceindexer.ai/mcpClaude Desktop and claude.ai
Settings > Connectors > Add custom connector
Name: inferenceindexer-mcp
Remote MCP server URL: https://api.inferenceindexer.ai/mcpCursor
{
"mcpServers": {
"inferenceindexer-mcp": {
"url": "https://api.inferenceindexer.ai/mcp"
}
}
}VS Code
code --add-mcp '{"name":"inferenceindexer-mcp","type":"http","url":"https://api.inferenceindexer.ai/mcp"}'Streamable HTTP
pypi: inferenceindexer-mcp 0.1.1Package only
PyPI (uvx)
claude mcp add inferenceindexer-mcp -- uvx inferenceindexer-mcp==0.1.1Built your own server? run.zero hosts MCP servers on European infrastructure.
Tools
10 tools
Read-only | Idempotent | Open world | ||
|---|---|---|---|---|
compare_providersCompare 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).
| Not declared | Not declared | Not declared | Not declared |
explain_modelGet 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.
| Not declared | Not declared | Not declared | Not declared |
get_composite_historyGet SIT-Composite index history / trend over time.
Args:
days: History window in days (1-90, default 30).
Returns: historical composite index values.
| Not declared | Not declared | Not declared | Not declared |
get_composite_latestGet 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.
| Not declared | Not declared | Not declared | Not declared |
get_modelGet 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).
| Not declared | Not declared | Not declared | Not declared |
get_model_historyGet 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.
| Not declared | Not declared | Not declared | Not declared |
get_providerGet 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.
| Not declared | Not declared | Not declared | Not declared |
list_providersList all inference providers with model counts and price stats. | Not declared | Not declared | Not declared | Not declared |
recommend_modelsRecommend 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).
aa_min: Hard floor on the AA Intelligence Index score (quality gate).
use_case: Task profile: support | volume | extraction | summarization | coding | research.
prefer_callable: Demote models without a verified call recipe below callable ones.
limit: Max recommendations (1-20, default 5).
Returns: ranked recommendations with endpoint_config and ranking evidence.
| Not declared | Not declared | Not declared | Not declared |
search_modelsSearch 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 (optional).
limit: Max results (1-100, default 25).
sort: Sort key: blended | input | output | sit_score | sit_adjusted_price (default sit_adjusted_price).
Returns: models with input/output/blended $/M pricing, provider, tier.
| Not declared | Not declared | Not declared | Not declared |
Details
- Registry status
- Active
- Published
- Sep 1, 2026
- Updated
- Sep 1, 2026
- Website
- 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