model-finder-mcp 1.3.5

Find the cheapest AI model that passes your own tests, and keep it that way.
_{Claude · Gemini · OpenAI · Bedrock · Vertex AI · local models — published by Indigloo}

Install  ·  How it works  ·  MCP tools  ·  Quick start  ·  Privacy  ·  Security

How it works

flowchart LR
    A["Audit<br/><sub>models and SDKs in your repo</sub>"] --> B["Draft evals<br/><sub>from your prompts, PII dropped</sub>"]
    B --> C["Evaluate<br/><sub>your keys · pass rate · latency · cost</sub>"]
    C --> D["Rank<br/><sub>cost per passing answer</sub>"]
    D --> E["Route<br/><sub>cheapest first, escalate on failure</sub>"]
    F["Size<br/><sub>weights + KV cache vs your GPU</sub>"] -.-> C
    classDef step fill:#f3f0e8,stroke:#1c1b19,color:#1c1b19;
    classDef pick fill:#f3f0e8,stroke:#cd462a,color:#cd462a;
    class A,B,C,E,F step;
    class D pick;

Advisory only: nothing is deployed, your active model is never changed, and data goes only to providers you configure and approve. Personal data and secrets are blocked before sending by default; see DATA-PROTECTION.md for how this maps to India's DPDP Act.

Install

Requires Python 3.9+ on your PATH as python3 (see Windows). Nothing to pip install.

Gemini CLI only starts extension MCP servers in folders you have trusted; it asks the first time you open a project.

Manual MCP setup

{
  "servers": {
    "model-finder": {
      "type": "stdio",
      "command": "python3",
      "args": ["/absolute/path/to/EvalHarness/mcp/server.py"],
      "env": { "MODEL_FINDER_ROOT": "${workspaceFolder}" }
    }
  }
}

{
  "mcpServers": {
    "model-finder": {
      "command": "python3",
      "args": ["/absolute/path/to/EvalHarness/mcp/server.py"],
      "env": { "MODEL_FINDER_ROOT": "${workspaceFolder}" }
    }
  }
}

{
  "mcpServers": {
    "model-finder": {
      "command": "python3",
      "args": ["/absolute/path/to/EvalHarness/mcp/server.py"],
      "env": { "MODEL_FINDER_ROOT": "/absolute/path/to/your/project" }
    }
  }
}

Clone the repo once and replace /absolute/path/to/EvalHarness. Repository tools read only inside the workspace: the roots your client shares over MCP, else MODEL_FINDER_ROOT, else the server's working directory. A filesystem root or home directory is refused.

Windows

Codex, Copilot CLI and Cursor start the server as ./mcp/server.py, which relies on its #!/usr/bin/env python3 line; on Windows use the manual setup or uvx model-finder-mcp. If python3 is not on your PATH, the MCP server will not start. Install Python from python.org (tick "Add python.exe to PATH") and either create a python3 alias or use the manual setup with "command": "py" and "args": ["-3", "C:/path/to/EvalHarness/mcp/server.py"].

MCP tools

Ten read-only tools, so the same features work as tools in every supported IDE:

Every tool returns a readable markdown summary by default and the full result as structured JSON (response_format: "json" puts the JSON in the text too). Large results are paginated (limit, offset, has_more, next_offset), modelfinder_audit_models can filter by provider family or hard-coded IDs, and unknown arguments are rejected with the list of valid ones. The server never opens a network connection, never reads API keys and never writes files. Only eval_models.py and the generated router.py call model providers, with your keys, when you run them.

Quick start

In your IDE, ask: "Audit which AI models this repo uses, then draft evals from our prompts and test whether a cheaper model passes." The assistant uses the MCP tools to audit and draft, agrees checks with you, and asks before the paid run.

From a terminal:

cp skills/eval-models/assets/example-config.json my-eval.json   # fill in verified model IDs and prices
export ANTHROPIC_API_KEY=... GEMINI_API_KEY=... OPENAI_API_KEY=...
python3 skills/eval-models/scripts/repo_scan.py audit .            # what the repo uses
python3 skills/eval-models/scripts/repo_scan.py examples .         # draft examples from prompt files
python3 skills/eval-models/scripts/eval_models.py my-eval.json --dry-run
python3 skills/eval-models/scripts/eval_models.py my-eval.json --out results
python3 skills/route-models/scripts/make_routing.py --results results/results.json --guards guards.json --out routing
python3 routing/router.py routing/routing.json "your prompt"

Size a local model:

python3 skills/size-local-model/scripts/estimate_memory.py --params-b 8.03 --bits 16 \
  --layers 32 --kv-heads 8 --head-dim 128 --context 8192 --available-gib 24

Tests

python3 -m unittest discover -s tests -v

End-to-end tests run against a local mock of the OpenAI-compatible, Anthropic, and Gemini APIs, and drive the MCP server over stdio; no key or network access is needed. evaluations/ holds a 10-question agent evaluation of the MCP tools.

Releases

1. Bump the version everywhere (python3 tests/check_release_version.py lists any field that differs) and add a CHANGELOG entry.
2. Push a tag vX.Y.Z. release.yml runs the tests, checks the versions, builds the package, publishes it to PyPI with trusted publishing, then publishes server.json to the official MCP Registry.
3. One-time setup:
   • PyPI: add a pending trusted publisher for project model-finder-mcp (owner Abhiindigloo, repository EvalHarness, workflow release.yml, environment pypi), ideally from the Indigloo PyPI organization, and create the pypi environment in the repository settings.
   • MCP Registry (co.indigloo/model-finder, verified through the indigloo.co domain). On your own machine, with OpenSSL 3:

     openssl genpkey -algorithm Ed25519 -out mcp-registry-key.pem
     echo "TXT record for indigloo.co: v=MCPv1; k=ed25519; p=$(openssl pkey -in mcp-registry-key.pem -pubout -outform DER | tail -c 32 | base64)"
     echo "GitHub secret MCP_REGISTRY_PRIVATE_KEY: $(openssl pkey -in mcp-registry-key.pem -noout -text | grep -A3 'priv:' | tail -n +2 | tr -d ' :\n')"
     

     Add the TXT record at the apex of indigloo.co, create a repository environment mcp-registry, store the hex key as its secret MCP_REGISTRY_PRIVATE_KEY, then delete mcp-registry-key.pem or keep it in a password manager.

skills/                      shared Agent Skills (single source of truth)
mcp/server.py                local MCP server (stdio, standard library only)
.mcp.json                    MCP launch config for Claude Code and Cursor (${CLAUDE_PLUGIN_ROOT}); disabled as a
                             project config inside this repo by .claude/settings.json
mcp.json                     Agent Plugins MCP launch config (Codex, Copilot CLI, Cursor): runs ./mcp/server.py
server.json, pyproject.toml  MCP Registry entry and PyPI package (model-finder-mcp); pypi/ holds the package marker
glama.json                   Glama directory ownership
.github/workflows/           tests on every push; release.yml publishes tags to PyPI and the MCP Registry
.claude-plugin/              Claude Code (and Copilot CLI) plugin + marketplace manifests
.codex-plugin/               Codex plugin manifest
.agents/plugins/             Codex repository marketplace
.cursor-plugin/              Cursor plugin + marketplace manifests
gemini-extension.json        Gemini CLI extension (context: GEMINI.md)
plugin.json                  Agent Plugins manifest with OpenAI listing metadata
AGENTS.md, GEMINI.md         skill index for agents that read root instructions
PRIVACY.md, TERMS.md, SUPPORT.md  pages the marketplace listings link to
assets/                      logo, README plates, and DESIGN.md (the visual language)

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MIT License · © 2026 Indigloo Softwares Private Limited}