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tobedoit

Google Calendar MCP Server

by tobedoit
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.2.1

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is singular and clear.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern (create_event), and with no other tools to compare, consistency is inherently perfect.

    Tool Count2/5

    One tool is too few for a Google Calendar server, as it lacks essential operations like listing, updating, or deleting events, making it severely limited for typical calendar management tasks.

    Completeness1/5

    The tool set is severely incomplete; it only allows event creation without any way to view, modify, or manage existing events, leaving major gaps in calendar functionality.

  • Average 2.9/5 across 1 of 1 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
    • Last stable release on
    • 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.

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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

  • 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 of behavioral disclosure. While 'create' implies a write/mutation operation, the description doesn't disclose important behavioral aspects like required permissions, whether the event is immediately published, what happens with duplicate events, error conditions, or what the response contains. For a mutation tool with zero annotation coverage, this represents a significant gap in behavioral transparency.

    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, efficient sentence that gets straight to the point without any wasted words. It's appropriately sized for the tool's complexity and front-loads the essential information. Every word earns its place in this concise formulation.

    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?

    For a mutation tool with 7 parameters (including complex nested objects) and no annotations or output schema, the description is inadequate. It doesn't explain what happens after creation, error handling, authentication requirements, or the scope of the operation (personal vs shared calendar). The 100% schema coverage helps with parameters, but the overall context for using this tool remains incomplete.

    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 description mentions 'specified details' which implies parameters exist, but doesn't elaborate on what those details are. With 100% schema description coverage, the schema already documents all 7 parameters thoroughly. The description adds no meaningful parameter semantics beyond what's already in the schema, so it meets the baseline of 3 when schema coverage is complete.

    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 verb ('create') and resource ('calendar event'), making the purpose immediately understandable. However, it lacks differentiation from sibling tools, but since there are no sibling tools on this server, this limitation is less critical. The phrase 'with specified details' adds some specificity about what can be configured.

    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, prerequisites, or constraints. It simply states what the tool does without any context about appropriate usage scenarios, timing considerations, or limitations. With no sibling tools, the need for differentiation is reduced, but still lacks basic usage context.

    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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