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Show what else breaks or needs retesting if you change a file or function.

Use it before editing ingested code, or to review a change: it walks the code graph built at ingest. For "why is it built this way" ask query; to see which repos are ingested use list_repos. Returns {target, label, found, direct_callers, direct_callees, affected_files, affected_tests, transitive_nodes, transitive_count, paths, path_facts}; path_facts gives each sample path's hops, dependents and whether it ends in a test. found: false means the target is not in the graph, so check the spelling or ingest the repo.

Input Schema

NameRequiredDescriptionDefault
depthNoHow many call hops to follow out from the target, e.g. 3. Default 2; values below 1 count as 1. Higher finds more but returns a bigger result.
targetYesWhat you plan to change: a file path as ingested, e.g. "micrograd/engine.py"; a function as "file::name", e.g. "engine.py::backward"; or a graph node id or wiki page name. Required.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / depth / description
      Added value: +"How many call hops to follow out from the target, e.g. 3. Default 2; values below 1 count as 1. Higher finds more but returns a bigger result."
    • addedInput schema / properties / target / description
      Added value: +"What you plan to change: a file path as ingested, e.g. \"micrograd/engine.py\"; a function as \"file::name\", e.g. \"engine.py::backward\"; or a graph node id or wiki page name. Required."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

No annotations, so the description must carry the load—it does: explains it walks the ingest-time code graph, defines the 'found: false' failure mode with remediation, and describes output shape including path_facts semantics.

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-loads the purpose, then usage, then return shape and failure mode; a little verbose in enumerating return keys, but each section serves routing or interpretation.

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 an unannotated analysis tool with an output schema, the description covers purpose, usage, sibling routing, return semantics, and the not-found case—everything needed to call it correctly.

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 coverage is 100%, so baseline 3 applies; the description doesn't elaborate param formats beyond what the schema already documents (target and depth are fully described there).

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 (show what breaks/needs retesting) and resource (file or function), and explicitly differentiates from siblings by naming 'query' for rationale and 'list_repos' for repo listing.

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 explicit when-to-use ('before editing ingested code, or to review a change') and routes to alternatives for adjacent needs ('why is it built this way' → query; which repos → list_repos).

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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