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@contractspec/app.provider-ranking-mcp

v1.6.17

Published

**MCP server that exposes provider-ranking operations and resources for benchmark ingestion, ranking refreshes, and leaderboard queries.**

Downloads

2,722

Readme

@contractspec/app.provider-ranking-mcp

MCP server that exposes provider-ranking operations and resources for benchmark ingestion, ranking refreshes, and leaderboard queries.

What It Does

  • Hosts a ContractSpec MCP server over provider-ranking contracts and runtime registries.
  • Exposes benchmark ingest, custom benchmark run, and ranking refresh operations.
  • Publishes leaderboard and model-profile resources backed by the ranking store.
  • Enriches incomplete ranking data with metadata from @contractspec/lib.ai-providers.

Running Locally

From packages/apps/provider-ranking-mcp:

  • bun run dev
  • bun run start
  • bun run build

Usage

bun run dev

Architecture

  • src/server.ts boots the MCP server and transport wiring.
  • src/index.ts exposes the package entrypoints for reuse or embedding.
  • The server composes provider-ranking contracts, module orchestration, and MCP resource/tool exposure.

Public Entry Points

  • Exports . and ./server for embedding or direct server startup.
  • Primary MCP surfaces are benchmark-ingest tools, ranking refresh tools, leaderboard resources, and model-profile resources.
  • Export . resolves through ./dist/index.mjs.
  • Export ./server resolves through ./dist/server.mjs.
  • Export ./* resolves through ./*.

Local Commands

  • bun run dev — tsdown --watch
  • bun run start — node dist/server.js
  • bun run build — tsdown
  • bun run lint — bun run lint:fix
  • bun run lint:check — biome check .
  • bun run lint:fix — biome check --write --unsafe --only=nursery/useSortedClasses . && biome check --write .
  • bun run clean — rm -rf dist

Recent Updates

  • Replace eslint+prettier by biomejs to optimize speed.
  • Add latest models and align defaults.
  • Resolve lint, build, and type errors across nine packages.
  • Add AI provider ranking system with ranking-driven model selection.

Notes

  • The current implementation uses an in-memory store; restart the process to clear rankings and benchmark results.