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jeannassereldine

MCP Weather Server

by jeannassereldine
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: get_coordinates converts a city name to geographic coordinates, while get_forecast provides weather data for given coordinates. There is no overlap or ambiguity between these functions.

    Naming Consistency5/5

    Both tools follow a consistent 'verb_noun' naming pattern (get_coordinates, get_forecast) with identical verb style and snake_case convention throughout. The naming is perfectly uniform and predictable.

    Tool Count2/5

    With only 2 tools, this server feels severely under-scoped for a weather domain. A weather server should typically include current conditions, forecasts, historical data, alerts, and multiple location input methods. Two tools cannot provide meaningful coverage.

    Completeness2/5

    The tool surface is severely incomplete for a weather server. Missing essential operations like getting current weather conditions, temperature data, precipitation forecasts, weather alerts, or supporting direct city name input for forecasts. The workflow requires manual coordinate lookup before getting forecasts, creating unnecessary friction.

  • Average 2.9/5 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

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. While it states the return format (tuple), it doesn't mention error conditions, rate limits, authentication requirements, or what happens with invalid city names. For a tool with no annotation coverage, this leaves significant behavioral gaps.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is brief but poorly structured. The first sentence is clear, but the 'Args:' section is incomplete ('the city of' ends abruptly). While it's concise, the incomplete sentence undermines its effectiveness. Every sentence should earn its place, and the incomplete second sentence doesn't.

    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 simplicity (one parameter) and the presence of an output schema, the description is somewhat complete but has gaps. The output schema existence means the description doesn't need to explain return values in detail, but it should still cover behavioral aspects. With no annotations and minimal parameter documentation, it's adequate but with clear room for improvement.

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

    Parameters3/5

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

    The description adds minimal parameter semantics beyond what the schema provides. It mentions 'city: the city of' but this is incomplete and adds little value. With 0% schema description coverage and only one parameter, the baseline would be 4 for zero parameters, but here we have one undocumented parameter. The description doesn't compensate for the coverage gap.

    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: 'Returns the latitude and longitude of the specified city as a tuple.' This is a specific verb (returns) + resource (latitude and longitude) combination. However, it doesn't distinguish this tool from its sibling 'get_forecast' - both likely involve geographic data but serve different purposes.

    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 about when to use this tool versus alternatives. There's no mention of the sibling tool 'get_forecast' or any context about when coordinate retrieval is appropriate versus weather forecasting. The description only states what the tool does, not when to use it.

    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 full burden for behavioral disclosure. It states what the tool does but doesn't describe important behavioral aspects: whether this is a read-only operation, what data format is returned, if there are rate limits, authentication requirements, or error conditions. The description is minimal and lacks operational context.

    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 appropriately concise with a clear purpose statement followed by parameter documentation. The two-sentence structure is efficient with no wasted words. However, the 'Args:' formatting is slightly redundant since parameters are already documented in the schema, preventing a perfect score.

    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 has an output schema (which handles return values) and simple parameters, the description is minimally adequate. However, for a weather forecasting tool with no annotations, users would benefit from more context about forecast type, time horizon, data sources, or common use cases. The description meets basic requirements but leaves important questions unanswered.

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

    Parameters3/5

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

    The schema description coverage is 0%, so the description must compensate. It explicitly lists both parameters (latitude, longitude) and their purpose ('of the location'), which adds value beyond the bare schema. However, it doesn't provide format details (e.g., decimal degrees), valid ranges, or examples. The baseline would be lower without this parameter listing.

    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 with 'Get weather forecast for a location' - a specific verb ('Get') and resource ('weather forecast'). It distinguishes from the sibling tool 'get_coordinates' by focusing on forecast rather than location data. However, it doesn't specify what type of forecast (e.g., daily, hourly, current) or time range, keeping it from a perfect score.

    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. It doesn't mention the sibling tool 'get_coordinates' or suggest when one might be preferred over the other (e.g., use get_coordinates first to obtain coordinates, then get_forecast). There's no context about prerequisites or limitations.

    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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