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Hemanth-hexo

GitHub Discovery MCP Server

by Hemanth-hexo

search_github_repos

Find open-source GitHub repositories matching your research topic or project idea, ranked by popularity and maintenance activity.

Instructions

Search GitHub for open-source repositories relevant to a research topic, technology, or project idea. Returns the top matching repos ranked by a blend of popularity (stars) and how recently they've been maintained, each with a short summary of what makes it useful. Good for requests like 'find me RAG implementation repos' or 'show me containerization examples in Go'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWhat the user is building, researching, or learning, phrased as GitHub search terms, e.g. 'retrieval augmented generation vector database' or 'containerization examples'.
filtersNoOptional filters to narrow or broaden the search.

Schema Changelog

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

  1. First observedv2.0.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral disclosure burden, and it delivers: it reveals the ranking blend (stars and maintenance recency) and the return shape (a short usefulness summary per repo). It does not disclose rate limits or result-count behavior, but for a read-only search tool the disclosed traits are the key ones.

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?

Three sentences with no waste: action/scope, ranking+output behavior, and usage examples. Each sentence earns its place and the core behavior is front-loaded in the first sentence.

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?

For moderate complexity (2 params, one nested object, no output schema, no annotations), the description covers the essential gaps: return value shape and ranking rationale. It is not exhaustive — result count, pagination, and explicit sibling differentiation are absent — but nothing an agent needs to call it correctly is critically missing.

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 the baseline is 3; the schema already documents query phrasing and each filter parameter. The description's example queries add minor illustrative value but no new parameter information beyond what the schema provides.

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 specific verb and resource — 'Search GitHub for open-source repositories' — and gives the context (research topic, technology, project idea) plus example request phrasings. It stops short of 5 because it never names a sibling (e.g., search_by_topic) or explicitly says how it differs from them, though the ranking-and-summary output description partially distinguishes it.

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?

'Good for requests like...' with two concrete examples ('find me RAG implementation repos', 'show me containerization examples in Go') establishes clear when-to-use context. It lacks explicit when-not-to-use guidance or routing to alternatives among the eight sibling tools, so it does not earn a 5.

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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