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matjmiles

mcp-weather-tutorial

by matjmiles
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one converts city names to coordinates, the other retrieves forecasts from coordinates. There is no functional overlap.

    Naming Consistency5/5

    Both tools follow the same verb_noun snake_case pattern (geocode_city, get_forecast), making them predictable and easy to understand.

    Tool Count4/5

    With only 2 tools, the server feels minimal, but as a tutorial it is appropriately scoped for the basic workflow of converting a city name to coordinates then getting a forecast.

    Completeness2/5

    The server only provides geocoding and daily forecasts, omitting common weather features like current conditions, hourly forecasts, alerts, or historical data. This is notably incomplete for a general weather domain.

  • Average 4.6/5 across 2 of 2 tools scored.

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

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

  • 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 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

  • Behavior3/5

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

    No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as rate limits, idempotency, or output format. While it describes input behavior, it lacks details on response structure or side effects, which is adequate but minimal.

    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 concise at 7 lines, front-loads the main purpose, and each sentence adds value without redundancy. No wasted words.

    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 tool has an output schema (not shown) and only 3 simple parameters, the description covers inputs well. However, it lacks a summary of expected output fields or any note on measurement units, which would help complete the context for an agent.

    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?

    Schema description coverage is 0%, so the description compensates by explaining latitude with example (e.g., 39.74 for Denver), longitude similarly, and days with a range (1 to 16). It adds meaning beyond the schema, though it could specify coordinate format (decimal degrees) explicitly.

    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 the tool gets a daily weather forecast for coordinates, and explicitly distinguishes itself from the sibling geocode_city by stating it does not accept city names. The verb 'get' and resource 'forecast' are specific.

    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?

    The description provides explicit when-to-use and when-not-to-use guidance: to get a forecast for coordinates, and to use geocode_city first for city names. It also gives examples of coordinate formats, aiding correct invocation.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the burden. It explains the tool returns candidate matches due to ambiguity, implying a read-only lookup. However, it does not disclose potential side effects, rate limits, or authorization needs, which would be beneficial.

    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 concise with no filler. It front-loads the purpose, then adds essential context about ambiguity and usage. Every sentence is valuable and efficiently communicates what the tool does.

    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 has only two parameters and an output schema is present, the description is sufficiently complete. It explains why multiple results may be returned and how this tool fits into a workflow with its sibling, providing enough context for an AI agent.

    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 description coverage is 0%, so the description fully compensates. It explains the 'name' parameter with examples and describes 'limit' as the maximum number of candidate matches, adding meaning beyond the schema's type and default.

    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 the tool finds latitude and longitude of a city by name. It distinguishes itself from the sibling tool 'get_forecast', which accepts coordinates, making the purpose unambiguous.

    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 to use this before get_forecast, and explains that city names are ambiguous, so candidate matches are returned. Provides clear context for when to use and what to expect.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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