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Abdulreh4874

gdocs-comments-mcp

by Abdulreh4874
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Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools have completely distinct purposes: add_comment handles the core functionality of adding inline comments, while check_connection is a utility for verifying the session. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (add_comment, check_connection) using snake_case, making them predictable and easy to understand.

    Tool Count3/5

    With only 2 tools, the server feels thin. While the check_connection tool is a necessary utility, the server would benefit from additional comment management tools (e.g., list, reply, resolve) to be more self-contained.

    Completeness2/5

    The server only covers adding comments and checking connection, but lacks essential operations like listing, replying, resolving, or deleting comments. This is a significant gap for a tool set focused on Google Docs comments.

  • Average 4.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 17 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.

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

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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

  • Behavior5/5

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

    Discloses that it launches/attaches browser and probes docs.google.com, revealing network and browser activity. Returns specific fields. No annotations to contradict.

    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 with no wasted words. Covers purpose, behavior, return value, and follow-up action efficiently.

    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?

    Complete for a simple diagnostic tool with zero parameters and no output schema. Covers what it does, when to use, what to do on failure.

    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?

    No parameters exist, so description does not need to add parameter info. Baseline 4 applies as description is adequate for zero-parameter tool.

    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?

    Clear verb 'Check' and specific resource 'Google session behind this server'. Distinguishes from sibling 'add_comment' by focusing on connection diagnosis rather than adding comments.

    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?

    Explicitly states when to use: 'Call this to diagnose failures'. Provides actionable follow-up if not connected. No exclusion for alternatives, but sibling tool is clearly different.

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

  • Behavior5/5

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

    Despite no annotations, the description fully discloses behavioral traits: it drives a real browser session, requires prior login, and returns only specific fields (ok, anchored, occurrence_used, verified). It also clarifies that anchoring is only possible through this tool, not via Google APIs directly.

    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 front-loaded with the primary purpose and action. It contains all necessary information but has slight redundancy (e.g., mentioning the browser session twice). Could be tightened slightly without losing clarity.

    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?

    With 4 parameters, no output schema, and no annotations, the description covers all user needs: prerequisites, behavior, return values, and distinction from alternatives. No critical gaps remain for an AI agent to misuse the tool.

    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 coverage is 100%, but the description adds extra value: explains doc accepts ID or full URL, find_text must be exact and unique, occurrence is 1-based, and comment_text is plain text with newlines. It also describes behavior when find_text is omitted.

    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 adds an inline comment anchored to a text fragment in a Google Doc, with concrete examples of usage scenarios. It distinguishes itself from sibling tool check_connection and other comment operations like list/reply/resolve/delete that use the Drive API.

    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 when to use (leaving feedback on specific passages) and when not to use (list/reply/resolve/delete operations via Drive API). Mentions required human login step and optional omission of find_text for unanchored comments. Provides clear context for decision-making.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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