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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct aspect: 'do' performs actions on Google Workspace, 'fetch' retrieves specific content to a local directory, and 'search' finds items across services. No overlap in primary purpose.

    Naming Consistency5/5

    All tool names are single, lowercase verbs ('do', 'fetch', 'search'), creating a uniform and predictable pattern. No mixing of styles.

    Tool Count4/5

    Only 3 tools, but each is highly capable (e.g., 'do' encompasses many sub-operations). The count is slightly low for the broad domain, but the tools are well-scoped.

    Completeness4/5

    Covers essential operations: create, edit, fetch, search, share, and email. Minor gaps like delete/trash are absent, but the core workflows are supported.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 324 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 failing
  • 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?

    No annotations are provided, so the description carries the full burden. It mentions auto-detection of content type but does not disclose behavioral traits such as whether the operation is read-only, side effects, authentication requirements, or rate limits. The description is insufficient for a tool with no annotation support.

    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 brief and front-loaded with the main purpose. Every sentence adds value, and there is no redundant information. It uses a clear, bullet-like structure that is easy to scan.

    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 presence of an output schema (which explains return values) and the use of examples for all parameters, the description is reasonably complete. However, it lacks explanation of what 'mise/' is and does not clarify the overall workflow, leaving some potential ambiguity for an unfamiliar agent.

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

    Parameters5/5

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

    Schema description coverage is 0%, but the description provides meaningful context for all parameters: base_path (set to cwd), attachment (for specific Gmail attachments), recursive (for folder trees), tabs (for spreadsheet tabs). file_id is implied but its role is clear from the tool purpose. This fully compensates for the lack of schema descriptions.

    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 fetches content and auto-detects types like Drive file, Gmail thread, and folder. It is specific about the resource (content to mise/) but does not differentiate from sibling tools (do, search) which may have overlapping functionality.

    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 usage hints (e.g., pass base_path=cwd, use attachment for Gmail attachments) but does not explicitly state when to use this tool versus alternatives or when not to use it. The guidance is implied through examples.

    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?

    With no annotations provided, the description carries the full burden and discloses key behaviors like auto-prefixing '[agent] ' for replies, setting 'confirm=True' to execute shares, and the OAuth setup process. It lacks explicit warnings about destructive actions (e.g., 'overwrite' fully replaces content) and does not mention rate limits or authorization beyond setup.

    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 well-structured, beginning with a general purpose and then detailing operations with bullet-like clarity. It front-loads the main intent and avoids redundancy, though some minor repetition (e.g., listing operations twice) could be trimmed without losing value.

    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 complexity (23 parameters, 14+ operations) and no schema descriptions, the description covers major areas: create, edit, email, comments, share, move, and OAuth. It explains when parameters are required but could add more on error handling or return values for edge cases.

    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?

    Schema description coverage is 0%, but the description adds extensive meaning by grouping parameters per operation (e.g., 'Create: content + title + doc_type', 'Email: draft (to + subject + content)'). It explains the purpose of many parameters in context, though not every parameter (e.g., cc, remove) is individually described.

    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 'Act on Google Workspace' and enumerates specific operations (create, move, edit, draft/reply, organise Gmail). It distinguishes itself from siblings 'fetch' and 'search' by being the action-oriented tool, covering creation, modification, and email management.

    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 detailed context for each operation, specifying required parameters and behavior (e.g., 'Create: content + title + doc_type' for docs, 'draft (to + subject + content)' for emails). However, it does not explicitly state when not to use this tool versus alternatives, leaving some implicit understanding from the sibling names.

    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?

    Even without annotations, the description explains writing to files, returning path+summary, and non-recursive behavior. Could more explicitly mention that it creates files.

    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?

    Well-structured with sections for args and returns, but somewhat lengthy. However, all sentences add value for a complex tool.

    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?

    Covers all 6 parameters, return fields, edge cases (guest mode, non-recursive), and includes scope notes. Very complete given the tool's complexity and presence of output schema.

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

    Parameters5/5

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

    With 0% schema coverage, the description fully compensates by explaining each parameter's purpose, defaults, and interactions (e.g., folder_id forces sources=['drive']).

    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 it searches across Drive and Gmail, writes results to a file, and returns a path and summary. It distinguishes from siblings ('do', 'fetch') by being a search tool.

    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?

    Provides detailed when-to-use guidance for each parameter, including when query is optional, default sources, non-recursive folder_id behavior, and scope notes in 'cues'.

    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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