Model Finder

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Audit AI models in the repo

modelfinder_audit_models

Read-onlyIdempotent

List AI model IDs and SDKs in a repository with file locations, provider families, usage counts, and hard-coded versus environment-variable defaults to audit model usage.

Instructions

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.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`path`	No	Directory 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).
`limit`	No	Items per page (1-200, default 50).
`offset`	No	Items to skip; use next_offset from the previous page.
`hard_coded_only`	No	Only IDs not read from configuration or environment.
`provider_family`	No	Only models from this family, e.g. "anthropic".
`response_format`	No	markdown (default) for a readable summary, json for the full machine-readable result.	markdown

Output Schema

TableJSON Schema

Name	Required	Description	Default
`sdks`	Yes
`models`	Yes
`summary`	Yes
`pagination`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.3.5Oct 18, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and non-destructive, so the safety profile is covered. The description adds genuine behavioral context beyond that: it skips secret-like files and dependency folders, never returns line content, and results are sorted by usage and paginated. It stops short of detailing output shape or rate behavior, but the added filtering/redaction context is meaningful.

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?

Two tight sentences that front-load the returned content and then the behavioral constraints. Dense but well-ordered with no filler, though the second sentence bundles several distinct traits into one clause chain.

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?

For a read-only audit tool with a full output schema and 100%-covered input schema, the description supplies the remaining needed context: what is scanned, what is excluded for safety, sorting and pagination. An agent has enough to invoke it correctly without opening anything else.

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 every parameter is already documented in the schema, including enums, defaults and limits. The description restates output concepts (provider family, route hints) but adds no syntax or semantics beyond what the schema provides, making the baseline 3 appropriate.

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 (List) and resource (AI model IDs and AI SDKs) and enumerates the returned fields: locations, provider family, route hints, usage counts, hard-coded vs env-var. This clearly distinguishes it from sibling tools like modelfinder_scan_sensitive_data or modelfinder_estimate_cost.

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

Usage Guidelines3/5

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

The description establishes context (an audit of the repo) but never states when to choose this over siblings, nor any prerequisites or exclusions. Usage is implied rather than guided, so it sits at the minimum-viable level.

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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