Skip to main content
Glama
hzy9981

io.github.hzy9981/aviation-accident-analysis

by hzy9981
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: uploading and generating a prompt, listing templates, and listing reports. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    All tool names use snake_case and follow a verb-first pattern. The first tool uses a compound verb 'upload_and_generate' while the others use 'list_available', which is a minor deviation but still predictable and readable.

    Tool Count4/5

    Three tools is a slightly thin but reasonable set for the narrow scope of uploading reports and managing available templates/reports. It is within the acceptable range and does not feel excessive.

    Completeness2/5

    The tool surface is incomplete for a server named 'aviation-accident-analysis'. It only supports uploading a report and listing templates/reports, with no tools to actually perform analysis, manage templates (create/update/delete), or retrieve detailed content. This leaves significant gaps that would require external actions.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 21 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 only says 'list,' which implies a read operation, but it does not mention permissions, what 'available' means, whether the search is recursive, or any side effects. The description adds no behavioral context beyond the core verb.

    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, concise sentence that directly states the purpose without unnecessary words. It is front-loaded with the action and clearly conveys the tool's function.

    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?

    For a simple list tool, the description is adequate in stating the core function, but it lacks usage guidelines, exclusions, and any detail about the output structure (although an output schema exists). Given the ambiguity with siblings and lack of annotations, the description is not fully complete, but it is not severely deficient for such a simple operation.

    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 input schema has one parameter 'directory' with no description (0% schema coverage). The description partially compensates by explaining that reports are listed in a specified directory, but it does not clarify the expected format or any constraints on the directory path. This is minimal but non-trivial compensation.

    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 action (list) and the resource (available reports) with a scope (specified directory). It is distinct from sibling tool list_available_templates, which focuses on templates, so the purpose is unambiguous.

    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?

    There is no guidance on when to use this tool versus alternatives like list_available_templates or upload_and_generate_prompt. The description provides no exclusions, prerequisites, or context that would help an agent choose this tool over others.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the burden. It discloses the main behavioral steps (upload, parse, wrap) but does not mention side effects, permissions, or whether the file is stored. This is adequate for a simple tool but leaves some uncertainty.

    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, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It front-loads the main action and includes only essential details.

    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?

    The tool is relatively simple, the schema covers all parameters, and an output schema exists. The description is complete enough for an agent to know how to invoke it, though it could mention that the template must exist (a gap covered by sibling tools).

    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 schema covers both parameters with full descriptions (100% coverage). The description adds useful context about accepted file types, which goes slightly beyond the schema, but it does not significantly enhance understanding of the parameters.

    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 action (upload), the resource (aviation accident report), accepted formats (PDF, DOCX, TXT), and the processing steps (parse and wrap in a prompt template). It distinguishes itself from sibling tools that only list templates or reports.

    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 implies when to use this tool: when you have a report file to upload and need to generate a prompt. Context is clear, though it does not explicitly mention alternatives like using 'list_available_templates' if only browsing templates.

    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 must carry the full burden of behavioral disclosure. It only states the action and directory, but does not explicitly confirm the operation is read-only, mention any limitations (e.g., empty directory behavior), permissions, or side effects. The verb 'List' implies non-mutating behavior, but the description lacks explicit transparency about behavioral traits.

    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 sentence that is front-loaded with the action (List) and resource (prompt templates), and adds the directory location. Every word is necessary and there is zero fluff.

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

    Completeness5/5

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

    For a simple list tool with no parameters and an existing output schema, the description is complete. It clearly specifies what is listed and where, and the sibling context (list_available_reports) makes the differentiation obvious without needing extra detail.

    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?

    This tool has zero parameters, so the baseline is 4. There are no parameter semantics to clarify beyond the schema, which is already complete (100% coverage).

    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 lists all available prompt templates in the prompts/ directory, using the specific verb 'List' and a precise resource. It naturally distinguishes itself from siblings like list_available_reports (reports vs. templates) and upload_and_generate_prompt (listing vs. generating).

    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 provides clear context—listing prompt templates from a specific directory—which implicitly tells the agent when to use this tool over siblings. However, it does not explicitly mention exclusions or alternatives, such as 'use list_available_reports for reports', so it falls short of a 5.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

aviation-accident-analysis MCP server

Copy to your README.md:

Score Badge

aviation-accident-analysis MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/hzy9981/aviation-accident-analysis'

If you have feedback or need assistance with the MCP directory API, please join our Discord server