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gustavodetoni

MCP Issue

by gustavodetoni
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TypeScript
Hybrid

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one creates GitHub issues via Octokit, while the other returns a diff of the current repository. There is no overlap in functionality or ambiguity between them.

    Naming Consistency4/5

    Both tools use snake_case naming, which is consistent. However, one tool includes 'github' in its name while the other does not, creating a minor deviation in naming pattern. The verbs 'create' and 'git_diff' (which is more of a noun phrase) are not perfectly aligned in style.

    Tool Count2/5

    With only 2 tools, the server feels thin for a domain that appears to involve GitHub and repository operations. This limited set does not provide comprehensive coverage for typical workflows, such as reading or updating issues, managing repositories, or handling other common tasks.

    Completeness2/5

    The tool set is severely incomplete for a server named 'MCP Issue' and including GitHub-related functionality. There are significant gaps: no tools to list, get, update, or delete issues, and no broader GitHub operations like managing pull requests or repositories. This will likely cause agent failures in many scenarios.

  • Average 2.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

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  • 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 .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool creates an issue (implying a write/mutation operation) but doesn't mention authentication requirements, rate limits, error conditions, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

    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?

    The description is a single sentence that directly states the tool's function. It's appropriately brief and front-loaded with the core purpose. While it could be more informative, it doesn't waste words or include unnecessary details.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a mutation tool with 6 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain parameter meanings, behavioral expectations, or return values. The agent would struggle to use this tool correctly without additional context or trial-and-error.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage and 6 parameters (5 with defaults, 1 required), the description provides no information about any parameters. It doesn't explain what 'owner', 'repo', 'title', 'body', 'labels', or 'assignees' mean or how they should be formatted. The description fails to compensate for the complete lack of schema documentation.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool's purpose ('Cria uma issue no GitHub') which translates to 'Creates an issue on GitHub', providing a clear verb+resource. However, it doesn't differentiate from the sibling tool 'git_diff' or specify what makes this tool unique. The mention of 'usando Octokit' adds implementation detail but doesn't enhance functional distinction.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, when-not-to-use scenarios, or comparison with the sibling tool 'git_diff'. The agent must infer usage from the tool name alone.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool returns a diff for understanding local changes, but lacks details on behavioral traits such as whether it requires specific Git states, how it handles errors, if it's read-only or has side effects, or any rate limits. This is a significant gap for a tool with 5 parameters and no annotation coverage.

    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 appropriately sized and front-loaded with the core purpose in the first sentence, followed by a brief usage note. Both sentences earn their place by providing essential information without redundancy or unnecessary details, making it highly efficient and well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity (5 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, parameter usage, or behavioral context needed for effective tool invocation. The description should do more to compensate for the lack of structured data, especially for a tool with multiple parameters and no output schema.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema description coverage is 0%, meaning none of the 5 parameters are documented in the schema. The description does not mention any parameters or add meaning beyond the schema, failing to compensate for the low coverage. This leaves the agent with no guidance on what parameters like 'base', 'head', 'mode', etc., mean or how to use them effectively.

    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 clearly states the tool's purpose: 'Retorna um diff do repositório atual' (Returns a diff of the current repository). It specifies the verb (returns) and resource (diff of current repository), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'create_github_issue', which serves a completely different purpose, so it doesn't fully earn a 5.

    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?

    The description provides implied usage guidance: 'Use para entender alterações locais' (Use to understand local changes). This suggests when to use the tool (for understanding local changes) but doesn't explicitly state when not to use it or mention alternatives. There's no comparison with the sibling tool or other potential tools, leaving some ambiguity in tool selection.

    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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