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codegraph_explain

Get an architecture overview of a project from its code graph, including stack, languages, directory layers, key patterns, top dependencies, and hub files.

Instructions

Architecture overview of a project from the code graph.

Returns structured data: stack, languages, directory layers, key patterns (mixins, base classes, CRUD schemas), top dependencies, and hub files.

Args: project: Project name (e.g. "my-app", "backend-api")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.6.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It clearly states that the tool returns structured data and lists the categories, which is useful. It does not explicitly say the operation is read-only, how the code graph is accessed, or describe failure behavior, though nothing suggests side effects.

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

Conciseness5/5

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

The description is compact and well-structured: a one-line purpose statement, a concise list of return categories, and a clearly labeled Args section. Every sentence adds value and there is no redundant information.

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

Completeness4/5

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

Since there is no output schema, the description compensates by enumerating the major return categories and giving an example for the single parameter. It is complete enough for correct invocation, but the absence of explicit read-only confirmation and sibling differentiation leaves a small gap.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate. It does by defining the only parameter: 'project: Project name (e.g. "my-app", "backend-api")', which provides both meaning and examples. For a single required string parameter, this is sufficient, though it could mention whether an ID, path, or exact name format is expected.

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

Purpose4/5

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

The description states a clear purpose: it provides an architecture overview of a project from the code graph, and it enumerates concrete return categories such as stack, languages, directory layers, key patterns, top dependencies, and hub files. It does not explicitly differentiate itself from sibling tools like codegraph_repomap or codegraph_stats, but the purpose is not vague.

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 intended use case is implied by 'Architecture overview' and the listed output categories, suggesting the tool is for high-level project understanding. However, there is no explicit when-to-use guidance, no exclusions, and no mention of alternatives among the many sibling tools.

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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