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blevinstein

Aviation Model Context Protocol

by blevinstein
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'get_notams' has a clear, distinct purpose focused on retrieving NOTAMs.

    Naming Consistency5/5

    The tool name 'get_notams' follows a clear verb_noun pattern (get + notams). Since there is only one tool, consistency is inherently perfect with no deviations or mixed conventions.

    Tool Count2/5

    The server has only one tool, which feels thin for a domain like aviation that typically involves multiple operations such as querying weather, flight plans, or airspace data. This minimal toolset limits functionality and suggests an incomplete surface.

    Completeness1/5

    The toolset is severely incomplete for an aviation context. It only provides NOTAM retrieval, with no coverage for other essential aviation data like METARs, TAFs, flight tracking, or airspace information, leading to significant gaps in agent workflows.

  • Average 2.9/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 1 community issues answered or closed 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, the description carries full burden but only states retrieval with filters. It lacks behavioral details like pagination handling (implied by pageNum/pageSize but not explained), rate limits, authentication needs, or response format implications (e.g., geoJson output). This leaves significant gaps in understanding tool behavior.

    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 front-loads the core action and scope without unnecessary words. It's appropriately sized for its purpose, with zero waste.

    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?

    For a complex tool with 17 parameters, no annotations, and no output schema, the description is inadequate. It doesn't address behavioral aspects like pagination, response formats, or error handling, leaving the agent with insufficient context 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?

    Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no extra meaning beyond implying filters exist, which the schema already details. Baseline 3 is appropriate as the schema handles parameter semantics effectively.

    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 ('Retrieves') and resource ('NOTAMs'), with scope ('based on specified filters'). It's specific about the action and subject matter, though without sibling tools to differentiate from, it can't achieve the highest score for sibling differentiation.

    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?

    No guidance is provided on when to use this tool, such as scenarios or prerequisites. The description mentions filters but doesn't explain their application or alternatives, leaving usage context unclear.

    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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