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PRIYADHARSANK

MCP Weather Project

by PRIYADHARSANK
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'get_alerts' has a clear and distinct purpose that cannot be confused with any other tool in this set.

    Naming Consistency5/5

    The naming pattern is trivially consistent as there is only one tool. The tool name 'get_alerts' follows a clear verb_noun convention (get + alerts), which would be consistent if more tools were added.

    Tool Count2/5

    A single tool is generally too few for a weather server's apparent scope, which typically includes forecasts, current conditions, and other weather data beyond just alerts. This feels thin and limited for the domain.

    Completeness2/5

    The tool surface is severely incomplete for a weather domain. It only provides alerts for US states, missing essential operations like getting forecasts, current weather, radar data, or international coverage, which will cause significant agent failures.

  • Average 3.2/5 across 1 of 1 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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  • 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?

    No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions the tool 'gets' data, implying a read-only operation, but fails to describe critical behaviors like error handling, rate limits, authentication needs, or what happens with invalid inputs. For a tool with zero annotation coverage, this leaves the agent with incomplete operational understanding.

    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 and front-loaded. The first sentence states the core purpose clearly, followed by a concise 'Args' section with essential parameter details. Every sentence earns its place with no redundant or verbose language.

    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 low complexity (1 parameter, no nested objects) and the presence of an output schema, the description is minimally adequate. It covers the purpose and parameter semantics but lacks behavioral context and usage guidelines. With no annotations, it should do more to explain operational aspects, but the output schema reduces the need to describe return values.

    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 meaningful context beyond the input schema. The schema only defines 'state' as a required string with title 'State', but the description clarifies it as a 'Two-letter US state code (e.g. CA, NY)', providing format examples and specificity. With 0% schema description coverage, this compensates well for the schema's lack of detail.

    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') and resource ('weather alerts'), with geographic scope ('US state'). However, with no sibling tools, it cannot demonstrate differentiation from alternatives, preventing 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, prerequisites, or exclusions. It only states what the tool does, with no context for usage decisions. This is a significant gap in helping an agent select the tool appropriately.

    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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