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wei9072

aegis

by wei9072
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Python
Local

Server Quality Checklist

75%
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  • Latest release: v1.0.1

  • Disambiguation5/5

    Each tool targets a distinct stage and scope: post-write attestation, pre-write single-file validation, and pre-write workspace-aware validation. No overlap in purpose.

    Naming Consistency5/5

    All tool names follow a clear verb_noun pattern: 'attest_path', 'validate_change', 'validate_change_with_workspace'. The naming is consistent and descriptive.

    Tool Count5/5

    Three tools cover the essential validation and attestation workflow without redundancy. The count is well-scoped for the server's purpose.

    Completeness4/5

    The tool set covers pre-write and post-write validation, but lacks a tool for batch workspace-wide analysis without a specific change. Minor gap for complete coverage.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

  • This repository includes a glama.json configuration file.

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

    Then . Browse examples.

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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

  • Behavior3/5

    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 discloses the tool performs cross-file checks (cycle detection, public symbol deletion) and is slower. However, it does not describe error behavior, side effects, or return values, leaving gaps in 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 three sentences, front-loaded with the key purpose, and every sentence adds value. No wasted words.

    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?

    The tool has 4 parameters, no output schema, and performs complex cross-file checks. The description fails to explain what the tool returns (e.g., success/failure, list of issues) and does not cover side effects or state changes. Incomplete for the complexity level.

    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 the schema already documents all parameters. The description adds no additional parameter-level meaning beyond what is in the schema. Baseline 3 is appropriate.

    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 is a workspace-aware gate that adds cross-file checks on top of validate_change, specifically detecting module import cycles and deletion of public symbols. It distinguishes itself from the sibling validate_change by noting it is slower and preferred for public API or shared module changes.

    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 explicitly advises to prefer this tool when the change touches a public API or shared module, and notes it is slower than validate_change, implying it is not for quick local checks. It does not explicitly state when not to use, but the guidance is sufficient.

    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?

    Discloses side effect: writes verdict to attestations.jsonl when workspace_root provided. Mentions reading on-disk content and running checks. Lacks details on error handling, permissions, or performance, but given no annotations, the description adequately discloses key behaviors.

    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?

    Two sentences: first defines action, second gives usage and side effect. No fluff, front-loaded with purpose. Efficient and well-structured.

    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 no output schema and no annotations, the description covers purpose, usage, and a key behavioral trait (writing audit log). Does not specify return value format but provides enough context for the agent to use the tool correctly.

    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 covers 100% of parameters. Description adds value for workspace_root (explaining it enables Ring R2 detection and audit log) beyond schema. For path, description repeats absolute nature but is clear. Overall adds significant meaning beyond schema.

    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?

    Clearly states the tool performs post-write attestation, reading file content and running specific checks (Ring 0 syntax, Ring 0.7 security, optional Ring R2 cycle). Distinguishes from siblings by mentioning 'post-write' and 'pre-write gate', contrasting with validate_change tools.

    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?

    Explicitly says when to use: from PostToolUse hooks, CI, or after any write bypassing the pre-write gate. Does not explicitly list alternatives but implies distinction from pre-write validation. Provides context for optional workspace_root enabling Ring R2 detection.

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

  • Behavior4/5

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

    Discloses it is a pure observation, no side effects, runs syntax, structural, cost, and security checks, returns decision without coaching. Lacks detail on output format but otherwise transparent.

    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?

    Description is concise (few sentences), front-loaded with purpose, and every sentence adds value without redundancy.

    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?

    Covers purpose, usage, parameters, and behavioral traits well. Minor gap: no output structure description, but given no output schema, it is mostly complete for a single-file validation tool.

    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?

    All parameters are described in the schema at 100% coverage; the description adds limited additional context (e.g., old_content enables cost regression), but baseline is appropriate.

    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?

    Description clearly states the tool validates a proposed file write using three rings, returns a decision without applying changes, and distinguishes itself from the sibling 'validate_change_with_workspace' by being single-file and speed-oriented.

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

    Usage Guidelines5/5

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

    Explicitly states when to use: 'when the change is contained to one file or when speed matters more than cross-file safety', implicitly guiding against the workspace variant.

    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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