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jknithin36

github-insights-mcp

by jknithin36
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TypeScript

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves stats for a known repository, the other discovers repositories by keyword. There is no overlap in functionality or ambiguity about which to use.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: get_repo_info and search_repos. The verbs 'get' and 'search' clearly reflect the action, and the nouns match the resource type.

    Tool Count3/5

    With only two tools, the server feels thin for a topic like GitHub insights. However, the tools cover the two most common entry points (lookup and discovery), so it is borderline but not unreasonably sparse.

    Completeness3/5

    The server covers basic repository stats and search but lacks additional insight-oriented capabilities such as comparing repos, fetching contributor activity, or listing recent releases. These are notable gaps but not fatal for simple use cases.

  • Average 4.2/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 5 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  • If you are the author, simply .

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses that the tool works on public repositories and lists the returned data points, which is useful. However, it does not discuss rate limits, authentication requirements, or any error behavior, leaving some transparency gaps.

    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?

    The description is exactly two sentences and every word contributes: the first sentence defines the tool's output, and the second defines its usage context. No unnecessary elaboration or repetition of schema details.

    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 a simple two-parameter tool with no output schema, the description lists the exact fields returned (stars, forks, language, etc.), which compensates for lacking an output schema. It also gives usage context. Minor gaps include no mention of error handling or rate limits, but the tool's simplicity makes this largely sufficient.

    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?

    The input schema already provides descriptions for both parameters ('owner' and 'repo') with examples, achieving 100% coverage. The description adds no additional parameter-level semantics beyond what's in the schema, so the baseline score of 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?

    The description clearly states the tool's function: 'Get key stats about a public GitHub repository' and enumerates the specific stats returned. The use-case sentence 'when the user asks about a specific repo' distinguishes this from the sibling 'search_repos' tool, which is for searching repositories.

    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?

    The description provides an explicit when-to-use statement: 'Use when the user asks about a specific repo's popularity or details.' However, it does not address when not to use it or explicitly name the alternative sibling 'search_repos', so it lacks a full exclusion/alternative comparison.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the full burden. It discloses that results are limited to public repos and returns top matches by stars, which is useful behavioral context. However, it doesn't mention result count, pagination, or output format.

    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?

    Two concise sentences front-load the purpose and then provide usage guidance. No redundant information or wasted words.

    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 a simple tool with one parameter and no output schema, the description gives enough information to select and invoke it. It covers purpose, usage alternative, and a key behavioral trait, but lacks details like how many results are returned.

    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%, and the 'query' parameter is already described with an example. The description's 'by keyword' aligns with the schema but doesn't add extra semantic meaning beyond what's in the structured data.

    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 'Search public GitHub repositories by keyword' – a clear verb, resource, and scope. Distinguishes from sibling get_repo_info by focusing on discovery rather than lookup.

    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 says 'Use when the user wants to discover repos on a topic rather than look up a specific one,' which directly contrasts with the sibling tool and gives clear usage context.

    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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  • Evaluate tool definition quality.

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