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socraticsurge

mcp-server-panchangam

Server Quality Checklist

75%
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  • Latest release: v1.13.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or overlap.

    Naming Consistency5/5

    With a single tool, the naming convention is inherently consistent.

    Tool Count1/5

    A panchangam server with only one tool is extremely limited; typical panchangam APIs offer many more elements like daily tithi, nakshatra, etc.

    Completeness1/5

    The tool only retrieves special days in a month, missing the vast majority of panchangam data (daily calendars, muhurta, etc.), making the server severely incomplete for its domain.

  • Average 4.3/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 362 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 failing
  • 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.

  • This repository includes a glama.json configuration file.

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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

  • Behavior4/5

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

    With no annotations, the description carries the full burden. It discloses key behaviors: the tool returns a JSON list, allows city name or coordinates, derives timezone if omitted, and defaults to the 'drik' system. It does not discuss authentication, rate limits, or error handling, but covers essential behavior for usage.

    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 sentence that efficiently conveys the purpose and major argument details. It is front-loaded with the main function. Although somewhat lengthy, it avoids unnecessary words and is well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the presence of an output schema (signaled), the description focuses on inputs and behavior. It covers the required parameters and key optional ones, explaining fallbacks. The overall usage context is complete for an agent to invoke the tool correctly.

    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 schema has 0% description coverage, so the description must compensate. It adds meaningful context for parameters: city can be name or lat/lon, timezone is derived automatically, system has default and possible values. It provides example values for year and month. While not all 7 parameters are fully detailed, the key ones are addressed.

    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 that the tool returns a JSON list of special days for a given month, explicitly listing types like Ekadashi, Amavasya, etc. This differentiates it from sibling tools that focus on other calendar events like eclipses or graha positions.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear usage context, specifying required arguments (year, month, city) and optional parameters with defaults. However, it does not explicitly state when to use this tool versus alternatives, though the list of special days implicitly guides selection.

    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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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.

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