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tobedoit

mcp-calendar-python

by tobedoit
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.3.5

  • Disambiguation5/5

    Only two tools with completely distinct purposes: creating events and health checking. No ambiguity.

    Naming Consistency5/5

    Both tools use snake_case naming, and the pattern is simple and predictable.

    Tool Count2/5

    With only 2 tools, the server is extremely minimal for a calendar service. Typically, a calendar server would need at least list, get, update, delete operations.

    Completeness2/5

    The server only provides create_event and health. Missing essential CRUD operations like listing events, updating, and deleting, which are necessary for a calendar domain.

  • Average 3.3/5 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
    • 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.

  • 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 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.

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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 burden. 'Simple health check' suggests a non-destructive, read-only operation, but does not disclose what the check entails (e.g., connectivity, database status), what is returned, or any side effects.

    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 phrase, front-loaded, and contains no extraneous information. It efficiently conveys the tool's 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?

    For a simple tool with zero parameters and an output schema, the description is nearly complete. However, it does not hint at the output structure or typical usage patterns, which could be helpful given the availability of a output schema.

    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?

    There are no parameters, and schema description coverage is 100%. The description does not need to add parameter information since none exist. Baseline 4 is appropriate.

    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 'Simple health check' clearly indicates the tool is a health-check endpoint. It distinguishes from the sibling tool 'create_event' which is about event creation, so purpose is clear but lacks specificity on what constitutes a health check.

    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 on when to use this tool versus alternatives (e.g., create_event). The phrase 'Simple health check' implies basic usage, but no explicit context or exclusions are provided.

    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?

    No annotations exist, so description must disclose behavior. It states creation and return of a confirmation string with link, but lacks side-effect details, permissions, or constraints (e.g., conflict handling, rate limits).

    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-organized with 'Args' and 'Returns' sections, each parameter on its own line. No fluff, but the list is long (10 items) which is acceptable for the parameter count.

    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?

    Covers purpose, parameter explanations, and return value adequately. However, lacks behavioral details like whether it can create events in the past, recurrence support, or attendee limits. With an output schema present, return format detail is unnecessary.

    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?

    With 0% schema coverage, the description adds significant meaning: specifies ISO format for times, explains each parameter's purpose (e.g., 'List of attendee emails'), and mentions default values and the effect of create_meet_link. However, 'reminders' remains vague (opaque object).

    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 'Create' and resource 'calendar event' with specific details. The description distinguishes from the only sibling tool 'health' which is unrelated.

    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 on when to use or not use this tool. No alternatives or exclusions provided, leaving the agent without usage context.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

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