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Model Finder

Model Finder

by Abhiindigloo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Audit AI models in the repo

modelfinder_audit_models
Read-onlyIdempotent

Lists AI model IDs and SDKs a repository uses, with file:line locations, provider families, usage counts, and hard-coded vs. environment-variable defaults. Use before comparing or replacing models.

Instructions

List the AI model IDs (quoted literals) and AI SDKs a repository uses, 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. Use first, to see which models a project depends on before comparing or replacing them; use modelfinder_draft_eval_examples to find test inputs instead. Read-only and local: no network calls, no files written. Skips secret-like files and dependency folders and never returns line content. Sorted by usage; paginate with limit/offset.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "modelfinder_audit_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathNoDirectory inside the workspace, relative to the project root (e.g. "services/api"). Defaults to the project root. If a call fails because the project is unknown, pass the absolute path of the project (your current working directory).
limitNoItems per page (1-200, default 50).
offsetNoItems to skip; use next_offset from the previous page.
hard_coded_onlyNoOnly IDs not read from configuration or environment.
provider_familyNoOnly models from this family, e.g. "anthropic".
response_formatNomarkdown (default) for a readable summary, json for the full machine-readable result.markdown

Output Schema

NameRequiredDescriptionDefault
sdksYes
modelsYes
summaryYes
paginationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.5

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is known; the description still adds real value by naming excluded inputs (secret-like files, dependency folders), the fact that no line content is returned, and the result ordering. The 'no network calls, no files written' line reinforces rather than contradicts annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the payload contents, then usage routing, then behavioral caveats and pagination – a logical order with almost no filler. It is dense in a single paragraph, but each clause carries distinct information rather than restating the name or title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return shape need not be spelled out, and the description still covers scope, exclusions, ordering and pagination for a six-parameter read tool with all-optional arguments. Nothing an agent needs in order to call this correctly and interpret the results is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so path, limit, offset, hard_coded_only, provider_family and response_format are all documented in the schema; baseline is 3. The description adds only the ordering ('Sorted by usage') and the pagination pattern ('paginate with limit/offset'), which are useful but do not explain any individual parameter beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('List the AI model IDs ... and AI SDKs a repository uses') and enumerates the returned fields: file:line locations, provider family, route hints, usage counts, hard-coded vs env-var origin. That is far more than the title's generic 'Audit AI models' and makes it distinguishable from siblings like estimate_cost or generate_routing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit sequencing rule ('Use first, to see which models a project depends on before comparing or replacing them') and names a concrete alternative for a different need ('use modelfinder_draft_eval_examples to find test inputs instead'). Both when-to-use and when-to-use-something-else are covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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