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stanislawherjan1

gdocs-comments-mcp

by stanislawherjan1
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

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 are completely distinct: add_comment performs the core action of adding anchored comments, while check_connection is a diagnostic for session health. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the consistent verb_noun snake_case pattern (add_comment, check_connection), making the API predictable and easy to navigate.

    Tool Count4/5

    With only two tools, the server is minimally scoped but fits a narrow purpose of adding anchored comments plus a connectivity check. It's slightly thin but not unreasonable for such a specific use case.

    Completeness2/5

    The server only supports adding comments; there are no operations to list, reply, resolve, or delete comments, which are core comment management features. This is a significant gap for a server dedicated to 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
    • 14 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.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool drives a real logged-in Docs session in a browser, requires a one-time human login, and returns only { ok, anchored, occurrence_used, verified } — never document content. This is substantive context beyond basic mutation semantics.

    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 somewhat lengthy but every sentence is purposeful: definition, usage, exclusions, prerequisites, and return value. It is front-loaded with the core purpose and then provides critical context. Slightly verbose but well-structured and not redundant.

    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?

    For a tool with no output schema, the description explains the return format and explicitly says what it never returns. It covers the API limitation, login requirement, alternative operations, and parameter usage nuance (find_text omission). This makes the tool's behavior fully predictable in different scenarios.

    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%, so the baseline is 3. The description adds minimal extra meaning: it reinforces that find_text can be omitted for an unanchored comment and explains the anchoring concept, but the schema already documents each parameter precisely. No significant additional semantics are provided.

    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: 'Add an inline comment anchored to a specific text fragment in a Google Doc'. It specifies the resource (Google Doc), the action (add anchored comment), and distinguishes it as the only way to place anchored comments due to API limitations, setting it apart from sibling tools.

    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?

    The description provides explicit usage guidance with 'USE THIS WHEN' and concrete examples like 'review this doc and comment on the weak spots'. It also states exclusions: 'Do NOT use for list/reply/resolve/delete — those work over the Drive API', and mentions the one-time login prerequisite, fully covering when and when not to use the tool.

    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?

    With no annotations, the description carries full burden. It discloses side effects (launches/attaches browser, network probe) and return value semantics. This is unusually transparent for a diagnostic tool.

    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 sentences with no waste. The first sentence states purpose and method, the second gives usage guidance. Front-loaded with the most important information.

    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?

    Given zero parameters and no output schema, the description fully covers the return shape and conditional action. It is complete for the tool's simplicity and context.

    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?

    The tool has zero parameters, so the baseline is 4. The description correctly omits parameter details and instead focuses on behavior and output, which 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 purpose: it checks if the Google session is usable by launching/attaching the browser and probing docs.google.com. It returns a structured result { connected, mode }, distinguishing it from the only sibling add_comment.

    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 instructs when to call: 'Call this to diagnose failures', and provides a clear follow-up action if connected=false. This gives the agent a complete decision path without needing alternatives.

    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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