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jsklan

devin-api-mcp

by jsklan
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.6.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: ask_question provides AI-powered answers, read_wiki_contents retrieves full documentation, and read_wiki_structure lists available topics. There is no meaningful overlap between asking questions and reading wiki docs.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern: ask_question, read_wiki_contents, read_wiki_structure. Naming is uniform and predictable.

    Tool Count5/5

    With only three tools, the server is tightly scoped to AI-powered repository Q&A and wiki access. This is well within the ideal range and each tool is essential to the core purpose.

    Completeness4/5

    The set covers the primary workflows: asking questions and reading wiki documentation. A minor gap is the lack of functionality beyond wiki content (e.g., general repository file browsing), but for the stated AI Q&A focus it is sufficient.

  • Average 3.3/5 across 3 of 3 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 is passing

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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, the description must convey behavioral traits, but it only says 'View', implying a read-only operation. It does not disclose what content is viewed (e.g., wiki vs README), error behavior, or output format. The lack of detail leaves significant ambiguity for the agent.

    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 concise sentence that is front-loaded with the verb. It is appropriately brief for a simple tool, though it could have included more specific terminology like 'wiki' to be more precise.

    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 tool name references 'wiki' but the description says 'documentation', there is ambiguity in the resource scope. The description does not clarify its relationship to sibling tools or mention any limitations, making it incomplete for reliable selection even though an output schema exists.

    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 fully describes the sole parameter repoName with a clear example. The tool description adds no additional parameter semantics, so the baseline of 3 applies due to high schema coverage.

    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 it 'View documentation about a GitHub repository', providing a specific verb and resource. However, it does not explicitly distinguish from sibling tools like read_wiki_structure, which likely reads the wiki structure rather than contents.

    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. Sibling tool names suggest a wiki-specific context, but no explicit context or exclusions are given.

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

  • Behavior3/5

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

    With no annotations, the description must carry behavioral transparency. It discloses that responses are AI-powered and context-grounded, which is useful. However, it does not mention limitations such as potential inaccuracy, access restrictions, or how the context is obtained.

    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 a single, clear sentence that states the action, target, and result without any wasteful words. It is optimally concise and front-loaded.

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

    Completeness3/5

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

    The tool is moderately complex (AI Q&A over repositories), and the description/schema together convey the essential information for invocation. However, it lacks behavioral nuance (e.g., how answers are generated, limitations) and does not reference the output schema, though that is optional when an output schema exists.

    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 descriptions cover both parameters with 100% coverage, so the description does not need to add parameter-level details. It adds minimal value by reinforcing the 'question' and 'repository' concepts, but nothing beyond the schema.

    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 identifies a specific action ('Ask any question') on a specific resource ('a GitHub repository') and the expected outcome ('AI-powered, context-grounded response'). It is distinct from sibling tools (read_wiki_contents, read_wiki_structure), though it does not explicitly reference them.

    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 implies usage when you need to ask a question about a repository, but it does not provide explicit guidance on when to prefer this tool over alternatives, nor does it mention any exclusions or prerequisites.

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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. 'Get a list' implies a read-only operation with no side effects, which is transparent, but it does not disclose any potential limitations, such as whether only top-level topics are returned or if the list is flat. It is adequate but minimal.

    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 a single, clear sentence with no filler. It fully conveys the core purpose without redundancy.

    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?

    Given the tool's simplicity (one parameter, output schema present), the description is nearly complete. It would benefit from a brief note about the output structure or when to use this versus sibling tools, but the essential information for selection and invocation is present.

    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 coverage is 100% with a clear description of repoName (owner/repo format). The tool description adds little beyond the schema, but the schema already provides sufficient meaning, so the baseline 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 a specific action ('Get a list') and resource ('documentation topics for a GitHub repository'). It distinguishes from sibling tools by focusing on structure/topics rather than content (read_wiki_contents) or Q&A (ask_question).

    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?

    No guidance is provided on when to use this tool versus alternatives. Sibling tool names suggest related functions, but the description does not mention them or any specific context like 'use this to browse available wiki pages before reading content.'

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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