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OleBo

Weather MCP Server

by OleBo
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_alerts retrieves weather alerts for a US state, while get_forecast provides weather forecasts for a specific geographic location. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with identical verb usage and snake_case formatting. This makes the tool set predictable and easy to understand.

    Tool Count2/5

    With only two tools, this server feels under-scoped for a weather domain. While the tools cover alerts and forecasts, there are obvious missing capabilities like current conditions, historical data, or radar imagery that would be expected from a weather service.

    Completeness2/5

    The tool surface is severely incomplete for a weather server. It lacks fundamental operations such as getting current weather conditions, accessing radar or satellite data, or retrieving historical weather information. Agents will face significant limitations when trying to perform comprehensive weather-related tasks.

  • Average 3.2/5 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

  • 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

  • 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 any behavioral traits: no information about rate limits, authentication requirements, response format, error handling, or whether this is a read-only operation. The description is purely functional without behavioral 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 two sentences: one stating the purpose and one explaining the parameter. It's front-loaded with the main purpose. The structure is clear, though the 'Args:' section formatting is slightly informal for MCP standards.

    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), the description covers the basic purpose and parameter semantics adequately. However, for a tool with no annotations and a sibling tool available, it should provide more context about when to use it versus alternatives and more behavioral information.

    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 description adds significant value beyond the input schema, which has 0% description coverage. While the schema only shows a 'state' parameter with type 'string', the description specifies it must be a 'Two-letter US state code' and provides examples ('e.g. CA, NY'). This clarifies the expected format that isn't evident from the schema alone.

    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 weather alerts for a US state.' It specifies the verb ('Get'), resource ('weather alerts'), and geographic scope ('US state'). However, it doesn't explicitly differentiate from its sibling tool 'get_forecast', which likely provides different weather information.

    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_forecast' or explain what distinguishes weather alerts from forecasts. There's no context about when alerts are needed versus general forecasts.

    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 only states what the tool does ('Get weather forecast') without mentioning any behavioral traits like rate limits, authentication requirements, data freshness, error conditions, or what the forecast includes (e.g., temperature, precipitation). This leaves significant gaps for an agent to understand how to use it effectively.

    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 sized and front-loaded with the core purpose in the first sentence. The parameter details are structured clearly under 'Args:' but could be slightly more concise by integrating recommendations into a single line per parameter. Overall, it's efficient with minimal waste.

    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 (2 required parameters) and the presence of an output schema (which handles return values), the description is partially complete. It covers the purpose and parameters well but lacks behavioral context and usage guidelines. With no annotations, it should provide more operational details to be fully helpful for an 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?

    The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explicitly lists both parameters (latitude and longitude) and provides practical guidance: 'recommended: up to 4 decimal places.' This clarifies precision expectations that aren't captured in the schema's basic type definitions, fully compensating for the schema's lack of descriptions.

    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 weather forecast for a location.' This specifies both the verb ('Get') and the resource ('weather forecast') with the target ('location'). However, it doesn't explicitly differentiate from the sibling tool 'get_alerts', which likely provides different weather-related information.

    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. There's no mention of the sibling tool 'get_alerts', nor any context about when a forecast is appropriate versus alerts. Usage is implied through the tool name but not explicitly stated.

    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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