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gograph_explain

Read-onlyIdempotent

Generates a synthesized, LLM-ready narrative for a Go symbol: role, callers, callees, complexity, cross-references, all in one structured document.

Instructions

Generate a synthesized, LLM-ready narrative for a Go symbol: role classification, callers, callees, complexity, SQL, env vars, HTTP routes, concurrency primitives, tests, and interface satisfaction — all in one structured document. The MCP server checks freshness before this call and refreshes in the current requested analysis mode; precise and precise_fallback graphs retry CHA/SSA after source changes. Read-only; no side effects. WHEN TO USE: For onboarding to an unfamiliar symbol, generating PR documentation, or getting an opinionated architectural assessment without issuing multiple tool calls. NOT TO USE: For raw source code (use gograph_source); for targeted blast-radius analysis (use gograph_impact). RETURNS: Rich structured JSON with role, narrative summary, and all associated cross-references; {"found":false} when symbol is not in the graph.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesThe name or ID of the symbol to explain (supports short name 'CreateUser', dot-notation 'graph.Graph', or fully-qualified ID)
Behavior5/5

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

The description states 'Read-only; no side effects,' which aligns perfectly with annotations (readOnlyHint=true, destructiveHint=false). It also discloses caching and freshness behavior, explaining that the server checks freshness and retries CHA/SSA after source changes, adding significant context beyond the 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?

The description is well-structured with clear sections and front-loads the purpose. It is slightly verbose in listing all included aspects, but each sentence adds value and does not waste space.

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?

Despite lacking an output schema, the description sufficiently describes the return value: rich structured JSON with role, narrative, cross-references, and a not-found indicator. Given the tool's complexity and number of siblings, the description is complete enough for an agent to invoke correctly.

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?

Only one parameter (symbol) exists with 100% schema description coverage. The description adds value by enumerating the supported formats (short name, dot-notation, fully-qualified ID), which is useful but not required. Baseline 3 is elevated to 4 due to this extra context.

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?

The description uses a specific verb 'Generate' and specifies the exact resource: a synthesized, LLM-ready narrative for a Go symbol with a detailed list of included aspects (role classification, callers, callees, etc.). It clearly distinguishes from siblings like gograph_source and gograph_impact by contrasting use cases.

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?

The description includes explicit 'WHEN TO USE' and 'NOT TO USE' sections, providing clear context for when the tool is appropriate (onboarding, PR docs, architectural assessment) and when alternatives like gograph_source or gograph_impact should be used instead.

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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