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xcollantes

MCP Template

by xcollantes
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation2/5

    The tools 'get_forecast' and 'get_weather' have significant overlap in purpose, as both retrieve weather forecast data from the National Weather Service for a location. The descriptions are similar, with 'get_forecast' specifying multi-period details and 'get_weather' being more general, which could cause confusion for an agent trying to select the appropriate tool. 'get_alerts' is distinct, focusing on active warnings rather than forecasts.

    Naming Consistency5/5

    All tool names follow a consistent 'verb_noun' pattern using snake_case: 'get_alerts', 'get_forecast', and 'get_weather'. There are no deviations in naming conventions, making the set predictable and easy to parse for an agent.

    Tool Count3/5

    With 3 tools, the count is borderline for a weather service server. It feels thin, as it lacks operations like updating or managing alerts, or providing historical data, which might be expected for a more comprehensive weather API. However, it covers basic retrieval needs, so it's not severely mismatched.

    Completeness2/5

    Inferring the domain as weather data access, there are significant gaps in the tool surface. The server only provides retrieval operations (get_alerts, get_forecast, get_weather) with no create, update, or delete capabilities, and it lacks tools for other common weather functions like radar data, historical trends, or location-based searches beyond coordinates. This incompleteness could lead to agent failures when more advanced operations are needed.

  • Average 3.7/5 across 3 of 3 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 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 provided, the description carries the full burden of behavioral disclosure. It mentions the data source ('National Weather Service API') but doesn't describe other behavioral traits such as rate limits, authentication requirements, error handling, or what specific forecast data is returned. The description is insufficient for a tool with no annotation coverage.

    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 efficiently structured in two sentences: one stating the tool's purpose and source, and another specifying the parameter format with an example. Every sentence adds value with no wasted words, making it appropriately concise and front-loaded.

    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?

    Given the tool's moderate complexity (1 parameter, no annotations, but with an output schema), the description is partially complete. It explains the parameter format but lacks behavioral context and usage guidelines. The presence of an output schema reduces the need to describe return values, but other gaps remain.

    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 input schema has 0% description coverage, but the description compensates by explaining the 'location' parameter's format: 'latitude,longitude (e.g., '47.7623,-122.2054')'. This adds meaningful semantics beyond the bare schema, though it doesn't cover all potential parameter nuances (e.g., coordinate ranges).

    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's purpose: 'Retrieves weather forecast information from the National Weather Service API for the specified location.' It specifies the verb ('retrieves'), resource ('weather forecast information'), and source ('National Weather Service API'), but doesn't explicitly differentiate it from sibling tools like 'get_alerts' or 'get_forecast'.

    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?

    The description provides no guidance on when to use this tool versus alternatives like 'get_alerts' or 'get_forecast'. It only states what the tool does without indicating appropriate contexts, exclusions, or comparisons to sibling tools.

    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 carries the full burden of behavioral disclosure. It describes what data is retrieved but lacks details on rate limits, authentication needs, error handling, or response format. It mentions the source (National Weather Service) but not potential limitations like data freshness or coverage.

    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 appropriately sized with two sentences that are front-loaded and efficient. The first sentence states the core purpose, and the second adds useful examples without redundancy. Every sentence earns its place by enhancing clarity.

    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's moderate complexity (single parameter, no annotations, but with an output schema), the description is mostly complete. It covers the purpose and scope well, but since there is an output schema, it does not need to explain return values. However, it could improve by addressing behavioral aspects like data source reliability.

    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 input schema has 0% description coverage, so the description must compensate. It adds meaning by specifying that the 'state' parameter refers to a U.S. state for which alerts are retrieved, though it does not detail format (e.g., abbreviations like 'CA') or validation rules. This partially compensates for the schema gap.

    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 specific action ('Retrieves'), resource ('active weather alerts, warnings, and advisories'), and source ('National Weather Service'), and distinguishes it from siblings by focusing on alerts rather than forecasts or general weather data. It provides concrete examples of alert types like severe weather warnings and flood advisories.

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

    Usage Guidelines3/5

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

    The description implies usage for retrieving active alerts in a U.S. state, but does not explicitly state when to use this tool versus alternatives like get_forecast or get_weather. It provides context (e.g., 'currently active'), but lacks explicit guidance on exclusions or prerequisites.

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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses the forecast includes temperature, wind conditions, and descriptions for 5 periods covering 2-3 days, which adds useful behavioral context beyond just 'retrieves forecast'. However, it doesn't mention rate limits, authentication needs, or error conditions.

    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?

    Two well-structured sentences with zero waste. The first establishes the core purpose and source, the second details the forecast content and temporal scope. Every element earns its place.

    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 (so return values are documented elsewhere) and no annotations, the description provides good context about what data is retrieved and the temporal scope. It could be more complete by mentioning coordinate format or validation, but covers the essential purpose well.

    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 must compensate. While it doesn't explicitly name the parameters, it specifies 'for the specified coordinates', which clearly implies latitude and longitude usage. This adds meaningful context beyond the bare schema, though it could be more explicit about parameter roles.

    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 specific action ('Retrieves'), resource ('multi-period weather forecast'), and source ('from the National Weather Service'). It distinguishes this tool from siblings by specifying it provides detailed forecasts rather than alerts or general weather data.

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

    Usage Guidelines3/5

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

    The description implies usage for detailed forecasts with coordinates, but doesn't explicitly state when to use this tool versus the 'get_weather' sibling. It provides context about what the forecast includes but lacks explicit guidance on alternatives or exclusions.

    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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