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BACH-AI-Tools

Open Weather13 MCP Server

by BACH-AI-Tools
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Python
Remote

Server Quality Checklist

42%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation2/5

    The tools have overlapping purposes that could cause confusion. v2_2 and v2_3 both get current weather data, differing only by input method (latitude/longitude vs. city name), which may lead to misselection. v2 provides forecast data, which is distinct, but the two current weather tools are ambiguous in scope.

    Naming Consistency2/5

    The naming is inconsistent and lacks a clear pattern. Tools are named v2, v2_2, and v2_3, which are opaque and do not follow a verb_noun convention. This makes it hard to infer functionality from names alone, though the descriptions provide clarity.

    Tool Count3/5

    With 3 tools, the count is borderline but reasonable for a weather API server. It covers current weather and forecast data, but the scope feels thin, as it lacks tools for other common weather operations like historical data or alerts, making it slightly under-scoped.

    Completeness2/5

    There are significant gaps in the tool surface for a weather domain. It only provides current weather (with two similar tools) and a 5-day forecast, missing operations like historical weather, air quality, alerts, or multi-location queries. This incompleteness could cause agent failures for broader weather-related tasks.

  • Average 2.9/5 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

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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. It mentions the tool fetches forecast data but lacks details on rate limits, authentication needs, error handling, or data format. The description is minimal and does not compensate for the absence of annotations, leaving key behavioral traits unspecified.

    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 a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose, making it easy to parse and understand quickly.

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

    Completeness2/5

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

    Given the complexity of a weather API tool with no annotations and no output schema, the description is insufficient. It lacks details on response format, error conditions, or any behavioral nuances. While the input schema is well-documented, the description does not provide enough context for an agent to fully understand how to use the tool effectively.

    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 semantic context beyond the input schema. It implies latitude and longitude are required for fetching weather data, but the schema already has 100% coverage with detailed descriptions (including examples for latitude/longitude and a language list for 'lang'). The description does not explain parameter interactions or provide additional insights, so it meets the baseline for high schema coverage.

    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: 'Get 5 days weather forecast data by Latitude & Longitude'. It specifies the verb ('Get'), resource ('weather forecast data'), and scope ('5 days', 'by Latitude & Longitude'). However, it does not differentiate from sibling tools v2_2 and v2_3, which likely offer similar or related weather data services.

    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 its siblings (v2_2, v2_3) or any alternatives. It states what the tool does but offers no context about prerequisites, limitations, or comparative use cases, leaving the agent to infer usage scenarios.

    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?

    No annotations are provided, so the description carries full burden. It mentions a documentation link but doesn't disclose key behavioral traits like rate limits, authentication requirements, error handling, or response format. The description is minimal and doesn't compensate for the lack of annotations.

    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, efficient sentence that front-loads the core functionality. It includes a documentation link which adds value without unnecessary verbosity. However, it could be slightly more structured by separating the link or adding brief context.

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

    Completeness2/5

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

    Given no annotations and no output schema, the description is incomplete. It doesn't explain what weather data is returned, response format, or error conditions. For a tool with 3 parameters and no structured output documentation, the description should provide more context about the tool's behavior and results.

    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?

    Schema description coverage is 100%, providing good documentation for all parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 without adding extra value.

    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 verb 'Get' and resource 'Current Weather Data', specifying the input method 'by Latitude & Longitude'. It's specific about what the tool does, though it doesn't explicitly differentiate from sibling tools v2 and v2_3, which likely have similar weather-related functions.

    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 sibling tools v2 and v2_3, nor does it specify any prerequisites, constraints, or scenarios where this tool is preferred over other weather data retrieval methods.

    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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions external documentation but doesn't describe key behaviors like authentication requirements, rate limits, error handling, or response format. 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 concise and front-loaded with the core purpose in the first phrase. However, the inclusion of a documentation link adds value but doesn't fully integrate into the description's flow. It's efficient but could be slightly more structured.

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

    Completeness2/5

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

    Given no annotations and no output schema, the description is incomplete for a tool that fetches data. It lacks details on response format, error cases, authentication, or how it differs from siblings. The external documentation link hints at more info but doesn't provide it directly, leaving significant gaps.

    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?

    Schema description coverage is 100%, so the schema fully documents both parameters (city and lang). The description adds no additional parameter semantics beyond what's in the schema, such as format constraints or examples. The baseline score of 3 reflects adequate but not enhanced parameter understanding.

    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: 'Get Current Weather Data by City Name' specifies the verb ('Get'), resource ('Current Weather Data'), and primary input ('by City Name'). However, it doesn't distinguish this tool from its siblings (v2 and v2_2), leaving ambiguity about how they differ.

    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 mentions external documentation but gives no context about prerequisites, limitations, or how it compares to sibling tools v2 and v2_2. Usage is implied only by the tool's name and description.

    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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