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emaratHossain

eventin-mcp-server

by emaratHossain
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Python
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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 are clearly distinct: get_bookings returns individual booking records with filters, while get_booking_stats provides summary statistics. There is no functional overlap, making selection unambiguous.

    Naming Consistency5/5

    Both tool names follow a consistent pattern: 'get_' followed by a noun, using snake_case (get_bookings, get_booking_stats). This is predictable and uniform.

    Tool Count3/5

    With only two tools, the server feels thin and below the typical well-scoped range of 3-15 tools. While both tools are relevant to bookings, the count is borderline and may be insufficient for broader event management needs.

    Completeness2/5

    The server only provides read and aggregate operations (list bookings, get stats). Missing are any create, update, cancel, or delete functionalities, which are essential for a complete booking management workflow. This represents significant gaps.

  • 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
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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, the description must fully disclose behavioral traits. It implies a read-only operation via 'Get' but does not explicitly state whether it is safe, what response format to expect, or any side effects. Pagination behavior and error handling are also omitted.

    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 concise and front-loaded with the main purpose, followed by a clean parameter list. No unnecessary words or repetition, though the formatting could be slightly more polished with markdown.

    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?

    The description covers the core functionality and filters, and an output schema exists to explain return values. However, it lacks usage guidance relative to get_booking_stats and does not mention pagination behavior or any limitations, leaving the context incomplete.

    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 description manually lists each parameter with a default or semantic meaning, such as 'status: Filter by payment status' and 'event_id: Filter by specific event ID', which compensates for the 0% schema description coverage. The schema only provides types and defaults, so the description adds useful context.

    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 tool retrieves event bookings with filtering, which identifies the primary function and resource. However, it does not explicitly differentiate from the sibling tool get_booking_stats, though the resource names are distinct enough.

    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 the sibling get_booking_stats. There is no mention of alternatives, prerequisites, or typical scenarios, leaving the agent without direction for tool selection.

    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 full responsibility for behavioral disclosure. It only mentions 'get', which implies a read operation, but does not describe permissions, side effects, limitations, or response characteristics. The output schema helps with return format, but behavioral transparency is sparse.

    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 concise sentence: 'Get summary statistics of bookings'. It is front-loaded with the verb and resource, with no filler or redundant information. Every word earns its place.

    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?

    The tool is simple (zero parameters) and has an output schema, so the description doesn't need to explain return values. However, it lacks usage context, limitations, or any behavioral notes, making it minimally viable but not comprehensive. Given the simplicity, it is acceptable but could be improved with guidance on using it alongside get_bookings.

    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 score is 4 per the guidelines. The description adds no parameter semantics because there are no parameters to describe, and the schema coverage is trivially 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 'Get' and the resource 'summary statistics of bookings'. It differentiates from the sibling tool get_bookings by indicating aggregated data rather than raw bookings, though it doesn't explicitly compare. It is specific enough to convey the tool's basic function.

    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 the sibling get_bookings. The description lacks any context about scenarios, prerequisites, or exclusions, leaving the agent without comparative direction.

    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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