Model Finder

Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/indigloo-co-model-finder

About

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Permissions

What this server can do and what it asks for, from its tool list, install config and code. Checked Oct 25, 2026.

Tools

6 read · 2 write · 0 delete · 2 not yet classified

Secrets

None asked for

Install scripts

None found

Runs as

Python package

Tools (10)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• modelfinder_audit_models

  List AI model IDs (quoted literals) and AI SDKs used in the repository, with file:line locations, provider family, route hints (Bedrock, Vertex AI, Azure, OpenRouter), usage counts and whether each ID is hard-coded or an environment-variable default. Read-only; skips secret-like files and dependency folders; never returns line content. Results are sorted by usage and paginated.

• modelfinder_check_output

  Run deterministic checks on one model reply. Use it to design eval checks and routing guards before spending money on a run.

• modelfinder_create_eval_config

  Assemble an eval config from candidate models, examples with checks, and settings; validate it and screen the examples for personal data and secrets. Returns the config to save as JSON for eval_models.py. Keys are referenced by environment variable name only.

• modelfinder_draft_eval_examples

  Draft eval examples from prompt files and fixtures (prompts/, evals/, fixtures/, *.prompt.md, and JSON/JSONL with input, prompt or chat messages). Drops any candidate with detected personal data or secrets and reports only categories and counts. Drafts have empty checks: agree checks with the user before modelfinder_create_eval_config. Paginated; ids are stable across pages.

• modelfinder_estimate_cost

  Planning estimate of monthly token cost from requests, average tokens per request and USD-per-million prices, with optional cached-input share and pass rate (cost per accepted result, counting retries). Prices must come from the provider's official pricing page.

• modelfinder_estimate_memory

  Estimate memory to run an open-weight model locally: weights at a precision plus KV cache for a context length and concurrency (standard MHA/GQA attention only), with headroom against available memory. Give all four of layers, kv_heads, head_dim and context to include the KV cache.

• modelfinder_generate_routing

  Turn a results.json object into a cheapest-first routing.json (with guards and a personal-data policy) and a LiteLLM proxy config. Returns both; nothing is written or deployed.

• modelfinder_rank_eval_results

  Read a results.json object and return the cheapest model that met the pass threshold, ranked by cost per passing call, plus the cost/reliability cascade and models dropped as dominated.

• modelfinder_scan_sensitive_data

  Detect Aadhaar (Verhoeff checked), PAN, payment cards (Luhn checked), email addresses, UPI IDs, Indian mobile numbers and API or private keys in text. Returns categories and counts only, never the values.

• modelfinder_validate_eval_config

  Check an eval config before running: schema, no keys in the config, HTTPS endpoints, no template placeholders, valid checks. Reports API call count, required environment variable names, unpriced models, and whether the personal-data guard would block the run. Makes no network calls.

Signals

Stars

3

Forks

1

Open issues

0

Weekly downloads

192

Last commit

Oct 18, 2026 (13 days ago)

Last release

Oct 17, 2026 (13 days ago)

Last package publish

Oct 17, 2026 (13 days ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

co.indigloo/model-finder

Version

1.3.5

Auth

No auth

Packages

pypi model-finder-mcp

License

MIT

Repository

github.com/Abhiindigloo/EvalHarness

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql