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elninyo-ops

baseline-mcp

by elninyo-ops
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The single tool's purpose is clearly defined.

    Naming Consistency5/5

    The single tool name 'get_climate_context' follows a clear verb_noun pattern. Consistency is trivial with one tool.

    Tool Count2/5

    A single tool is too few for what could be a broader climate data server. While the tool is detailed, one tool makes the server feel incomplete and limited in scope.

    Completeness2/5

    The tool only handles specific query forms (e.g., 'Will LOCATION be warmer...?') and excludes other natural language questions. There are clear gaps in functionality, such as providing raw data or different time ranges.

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

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

    • No community issues in the last 6 months
    • 16 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

  • This repository is licensed under MIT License.

  • 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

  • Behavior5/5

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

    No annotations present, so description must disclose all behaviors. It specifies 'land only', data sources (ERA5, WMO normals), and query format requirements. It also transparently notes that alternative phrasings will fail, giving clear behavioral expectations.

    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?

    Description is two efficient paragraphs: first states core purpose and data context, second provides vital usage guidelines. Every sentence adds value, no redundancy. Front-loaded with the essential function.

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

    Completeness5/5

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

    Given the tool's single parameter and presence of an output schema, the description covers all needed aspects: what it does, when to use, how to phrase queries, and failure modes. Nothing essential is missing.

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

    Parameters5/5

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

    Schema has 0% coverage, but description compensates thoroughly by detailing the exact required structure of the 'query' parameter, including multiple valid patterns and explicit examples. This adds essential semantic meaning beyond the bare schema.

    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?

    Description clearly states it provides statistically rigorous weather/climate context using forecast and historical percentiles. It explicitly contrasts with simple weather queries by emphasizing 'how unusual' conditions are, effectively distinguishing its purpose from basic weather tools.

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

    Usage Guidelines5/5

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

    Explicitly states when to use: needing unusualness relative to history. Provides exact query formats and warns against failing phrasings. No sibling tools exist, but description fully covers usage context and constraints.

    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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