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dataciviclab

dataciviclab

Official
by dataciviclab

github_get_discussion

Retrieves a GitHub Discussion by number and returns either the full or excerpted body, optionally including nested comments in a single request for complete thread context.

Instructions

Restituisce una GitHub Discussion. mode=summary restituisce solo un estratto del body (default excerpt_chars=500). mode=full restituisce il body intero (default, backward compat). include_comments=true aggiunge commenti e reply annidate nello stesso round-trip (comments_mode=full|summary, comments_limit, comments_excerpt_chars).

Input Schema

NameRequiredDescriptionDefault
modeNofull
numberYes
comments_modeNofull
excerpt_charsNo
comments_limitNo
repo_full_nameNodataciviclab/dataciviclab
include_commentsNo
comments_excerpt_charsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.9/5.0
Behavior4/5

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

No annotations exist, so the description carries the full burden and does substantial work: it discloses the default full-body behavior, backward compatibility, that summary truncates at excerpt_chars=500, and that include_comments pulls nested replies in the same round-trip (avoiding extra calls). It omits auth requirements, error behavior for a missing number, and any rate-limit or size caveats for full bodies.

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 dense paragraph with the resource statement front-loaded and each subsequent clause tied to a parameter or mode. No filler, though the packed parenthetical enumeration of comment parameters is harder to scan than a short bulleted form.

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 8-parameter read tool with an output schema (so return values need not be explained), the description covers mode selection, excerpt sizing, and nested-comment retrieval — everything needed to invoke it correctly. Only the unmentioned repo_full_name default is a residual 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?

With 0% schema description coverage, the description compensates by documenting 6 of the 8 parameters, including the mode values (full|summary), their defaults, and the comment-related knobs (comments_mode, comments_limit, comments_excerpt_chars). The notable gap is repo_full_name, which is never mentioned even though it defaults to a hard-coded repo (dataciviclab/dataciviclab) — an agent could silently query the wrong repository.

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 opens with a specific verb+resource ('Restituisce una GitHub Discussion'), which is immediately distinguishable from the sibling list tool github_list_discussions. It does not explicitly name the list sibling as an alternative, but the singular-resource scope makes the distinction clear.

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?

Usage is only implied: fetch one discussion, choose summary vs full and opt into comments. There is no explicit 'use this when... / do not use when...' guidance, and no mention of when to prefer github_list_discussions or how to route between summary and full modes (e.g. token budget).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.