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lichen911

Aviation MCP Server

by lichen911
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

    The two tools have clearly distinct purposes: one queries real-time aircraft data from ADS-B Exchange, the other queries the FAA registration database. There is no overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern ('query_aircraft', 'query_registration'), making them predictable and easy to understand.

    Tool Count4/5

    With only two tools, the server is minimal but covers two primary aviation data sources. It feels slightly thin but is reasonable for a focused purpose.

    Completeness4/5

    The server covers real-time tracking and registration lookup, which are core aviation queries. Minor gaps exist (e.g., historical data, airport info), but the main workflows are supported.

  • Average 3.8/5 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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  • 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 does not mention that this is a read-only operation, any rate limits, authentication requirements, or potential side effects. The agent is left without important safety context.

    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 consists of two concise, front-loaded sentences. The first sentence states the core action, and the second expands on what information is returned. No superfluous text.

    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 presence of an output schema, the description adequately covers the tool's purpose and parameters. However, it omits potential limitations (e.g., result count limits, pagination) which would be helpful for a query tool. It is minimally complete but not thorough.

    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?

    Schema coverage is 100%, and the description's parameter field provides concrete examples for 'value' (e.g., 'N12345', 'A004B3') and enumerates possible 'query_type' options with examples. This adds meaningful context beyond the schema's property descriptions.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly specifies the tool's action ('Query') and resource ('FAA aircraft registration database'), and details the kind of information returned (owner info, specs, status). Despite not contrasting with sibling tool 'query_aircraft', the purpose is unambiguous and specific.

    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 (e.g., 'query_aircraft'), nor does it mention prerequisites or exclusions. The agent lacks context to differentiate use cases.

    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. It mentions 'real-time' data, implying a read operation, but does not disclose rate limits, data freshness, authentication needs, or any limitations. Minimal behavioral transparency.

    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 two sentences long. The first sentence states the core purpose, and the second lists search criteria. Every sentence earns its place without redundancy. Front-loaded and efficient.

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

    Completeness4/5

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

    Given the tool has 5 parameters and an output schema exists, the description covers the query types and parameter usage well. It lacks usage caveats (e.g., rate limits, error handling) but output schema likely documents return structure. Overall complete for typical use.

    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?

    Schema coverage is 100%, baseline 3. The description adds significant value by explaining the meaning of each query_type and the conditions for 'value', 'radius', 'latitude', and 'longitude' parameters. It clarifies which parameters are needed for different query types, exceeding the schema's descriptions.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Query real-time aircraft data from ADS-B Exchange.' It lists specific search criteria, distinguishing it from the sibling 'query_registration' which likely focuses on registration lookups. The verb 'Query' and resource 'aircraft data' are specific.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explains when to use each query_type (e.g., 'callsign', 'military'), providing explicit context. However, it does not include when-not-to-use or compare directly with the sibling tool 'query_registration', which would improve guidance.

    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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