DevPilot MCP

Analyze GitHub issue

analyze_issue

Read-onlyIdempotent

Extract deterministic triage signals from a GitHub issue without an LLM, including type guesses, errors, stack frames, file paths, code blocks, and search queries.

Instructions

Deterministically extract triage signals from an issue (no LLM): a type guess (bug/feature/question/docs), error messages, parsed stack frames (JS and Python), mentioned file paths, fenced code blocks, and suggested queries to pass to search_codebase.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "issue_number": "<issue_number>"
    },
    "name": "analyze_issue"
  }
}

Input Schema

Name	Required	Description
`repo`	No	GitHub repository name. Optional, see "owner".
`owner`	No	GitHub owner (user or org). Optional: defaults to the workspace's "origin" remote (local mode) or the only allowlisted repo (remote mode).
`issue_number`	Yes	Issue (or PR) number, e.g. 42.

Output Schema

Name	Required	Description	Default
`repo`	Yes
`owner`	Yes
`title`	Yes
`type_guess`	Yes
`code_blocks`	Yes
`issue_number`	Yes
`stack_frames`	Yes
`type_signals`	Yes
`error_messages`	Yes
`mentioned_paths`	Yes
`suggested_search_queries`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 29, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, and openWorld, so the safety profile is covered. The description adds a genuine behavioral trait beyond that: the extraction is 'deterministic' and uses 'no LLM', telling the agent results are stable and reproducible. It doesn't discuss rate limits or failure modes, but the determinism claim is real added value.

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?

A single front-loaded sentence that leads with the verb and the determinism guarantee, then enumerates outputs. The enumeration is long but each item is informative and non-redundant; only the parenthetical nesting makes it slightly dense.

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?

An output schema exists, so return values need no explanation, yet the description helpfully names the signal categories anyway. Combined with annotations covering the safety profile and 100% schema coverage, an agent has everything needed to invoke it correctly, though nothing is said about behavior on invalid issue numbers or missing repos.

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 owner, repo, and issue_number are already fully documented in the schema, including the origin-remote fallback behavior. The description adds no parameter-level syntax or format guidance, so the baseline 3 is 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 and resource ('extract triage signals from an issue') and enumerates the exact outputs (type guess, error messages, stack frames, file paths, code blocks, suggested queries). The parenthetical '(no LLM)' plus the enumerated artifacts make it impossible to confuse with get_issue or get_docs.

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

Usage Guidelines4/5

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

It clearly signals the workflow context by noting the suggested queries are meant 'to pass to search_codebase', giving the agent a downstream routing cue. It stops short of stating when NOT to use it (e.g., vs. get_issue for raw issue body retrieval), so it is clear context without explicit exclusions.

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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