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gograph_errorflow

Read-onlyIdempotent

Trace a named error's definition, return sites, and propagation paths up the call graph to entry points for auditing end-to-end error handling.

Instructions

Trace how a named error sentinel or error message string is defined, returned, and propagates up the call graph toward HTTP handlers or CLI entry points. Requires .gograph/graph.json — run gograph build . first. Read-only; no side effects. Accepts either query (preferred) or term as the error name or message substring. WHEN TO USE: When auditing how a specific error is produced and handled end-to-end — find definition sites, all return sites, and upstream propagation paths (e.g., ErrNotFound). NOT TO USE: For general upstream traversal of any function (use gograph_callers or gograph_impact); for listing all error definitions (use gograph_errors). RETURNS: Definition sites, return sites, propagation path chains, and related test names; paths is empty when no propagation chain is found. Note: heuristic analysis — does not perform SSA or full data-flow tracking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
no_testsNoIf true, exclude test files from related-test collection (matches CLI --no-tests)
queryNoThe error string or sentinel error name (preferred over term)
termNoThe error string or sentinel error name (e.g., 'ErrInvalidToken' or 'invalid token')
Behavior4/5

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

Annotations already indicate read-only and idempotent; description adds that it requires a pre-built graph file, is heuristic analysis without SSA, and has no side effects—sufficient context beyond 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?

Well-structured with sections, front-loaded purpose, and each sentence adds value; slightly lengthy but justified by the tool's complexity.

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?

Given no output schema, description fully explains return types (definition sites, return sites, propagation paths, test names) and conditions (empty paths when no propagation found), plus prerequisites and limitations.

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?

With 100% schema coverage, description adds preference for 'query' over 'term', explains usage as error name or substring, and clarifies the 'no_tests' parameter's effect meaningfully.

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 clearly states the tool traces a named error sentinel or message string through definition, return, and propagation up the call graph, distinguishing it from siblings by specifying exact 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?

Explicitly provides WHEN TO USE and NOT TO USE sections, including alternative tools (gograph_callers, gograph_impact, gograph_errors) for different scenarios.

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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