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VatsKan

MCP Gmail Server

by VatsKan
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves unread emails, the other creates draft replies. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow the verb_noun pattern with snake_case, consistent and predictable naming.

    Tool Count2/5

    With only 2 tools for a Gmail server, the surface is too thin. A typical Gmail integration would require many more operations.

    Completeness1/5

    The tool set is severely incomplete for email management: missing send, search, archive, delete, label management, and other essential operations.

  • Average 3.2/5 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

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  • This repository includes a README.md file.

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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

  • Behavior2/5

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

    With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states the action without explaining side effects, permissions required, or whether the draft is saved automatically. The agent lacks information about the tool's mutability or safety profile.

    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 short sentence, which is concise and front-loaded. However, it may be too terse for the agent to understand context. It earns its place but could benefit from slight expansion.

    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's simplicity and the presence of an output schema, the description is minimal but lacks essential context about usage, parameter details, and behavioral traits. It does not fully enable correct selection and invocation.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description adds no meaning to the parameters 'message_content' and 'message_id'. It does not clarify what format or content is expected, or how to obtain the message_id. The schema already provides minimal info, but the description should compensate.

    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 'Create a draft reply to an email' uses a specific verb and resource, clearly indicating the action and the type of email operation. It distinguishes well from the sibling tool 'get_unread_emails', which is a read operation.

    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. The description does not mention prerequisites, context, or when not to use it. There is no mention of the sibling tool or alternative scenarios.

    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?

    With no annotations provided, the description bears the full burden of behavioral disclosure, but it fails to mention any behavioral traits such as authentication requirements, rate limits, or what happens when no unread emails exist. The output schema exists but the description does not leverage it to clarify return behavior.

    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 front-loaded sentence containing no extraneous words. It is concise and immediately communicates the core purpose.

    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?

    Given the tool has zero parameters and an output schema likely describing the return format, the description is minimally adequate but lacks important context such as whether it returns all unread emails or a subset, and whether pagination is involved. The description could be more complete, e.g., mentioning the scope or ordering.

    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, and the input schema coverage is 100% (empty). Baseline for zero parameters is 4, and the description adds no additional parameter details. It is adequate given the lack of parameters.

    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 'Get a list of unread emails' uses a specific verb 'Get' and clearly identifies the resource 'unread emails', leaving no ambiguity about the tool's function. It is distinct from the sibling tool 'create_draft_reply', which is a write operation.

    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 usage guidelines are provided. The description does not state when to use this tool versus alternatives, nor does it offer any context about prerequisites or contraindications. The sibling tool 'create_draft_reply' is mentioned in context but not in the description itself.

    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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