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Archonics MCP Audit Server

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by archonics
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Server Quality Checklist

67%
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  • Latest release: v0.1.5

  • Disambiguation5/5

    Each tool targets a distinct audit dimension: context packing, system prompt, and tool definition. There is no overlap in purpose, making it clear which tool to use for a given issue.

    Naming Consistency5/5

    All tool names follow a consistent 'audit_<target>' pattern using snake_case, which is predictable and clearly indicates the function of each tool.

    Tool Count5/5

    Three tools is appropriate for a specialized audit server, covering the core areas of context, system prompt, and tool definition without unnecessary bloat.

    Completeness4/5

    The server covers three critical audit aspects, but lacks a tool for full tool-set coherence or evaluation, which is noted as a paid-tier feature. Minor gap in free tier completeness.

  • Average 4.6/5 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
    • 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

  • Behavior4/5

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

    No annotations provided. Description discloses that findings cover content inventory, redundancy, freshness, ordering, truncation risk, and prompt-cache utilization. Also notes that literal dumps produce sharper findings. This is sufficient behavioral disclosure.

    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, directly stating purpose, usage, and output. No extraneous information. Well structured with key information front-loaded.

    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?

    No output schema present, but description explains the coverage of findings (6 aspects). Adequate for a tool that returns a simple text analysis. Could be improved by mentioning number of findings or format, but current is sufficient.

    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?

    Both parameters have descriptions in the schema. The description adds extra meaning by explaining what kinds of input are acceptable (literal or structured) and notes that 'literal dumps produce sharper findings,' which goes beyond the 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?

    The description states it analyzes context payload for efficiency, redundancy, and ordering. It names specific outputs (top 3 findings) and distinguishes from sibling audit tools by focusing on context packing.

    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 'Use this when a user is concerned about agent cost, latency, or quality degradation on long conversations.' It also clarifies acceptable input formats. No explicit exclusion criteria, but the context is clear.

    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?

    No annotations provided; description carries full burden. It discloses return format (structured JSON with severity-ranked findings), scope (top 3 findings), and areas covered. Could mention any limitations like rate limits or caching, but overall transparent.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Six sentences, each necessary. Purpose is front-loaded. Could trim minor redundancy but overall efficient.

    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?

    Given no output schema and moderate complexity, description covers purpose, usage, return format, coverage areas, and alternative options. Thorough for a free-tier scan tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, but description adds significant value: for 'system_prompt' it advises to paste full prompt and warns against redaction; for 'context' it explains purpose and impact of leaving blank. Exceeds schema detail.

    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 verb 'Analyzes' and the resource 'system prompt'. It distinguishes from sibling tools by focusing on system prompts, not context packing or tool definitions.

    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 a user shares a system prompt for review or debugging. Provides an alternative: direct to archonics.ai for full audit. No ambiguity.

    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?

    With no annotations provided, the description carries the full burden. It explains that the tool returns top 3 findings covering specific areas (description quality, parameter schema precision, etc.) and accepts various formats. While it does not mention potential errors or limitations, for a read-only analysis tool the description provides sufficient behavioral 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 is two sentences plus a brief list of coverage areas. It is front-loaded with the main action, contains no filler, and every sentence adds useful information. The structure is efficient and easy to parse.

    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?

    Given the tool's moderate complexity (2 parameters, no output schema), the description is complete. It explains what the tool does, when to use it, what it returns (top 3 findings), and the aspects it covers. It also provides guidance on alternatives, making it self-contained for an agent.

    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%, but the description adds value beyond the schema by specifying that the tool_definition parameter accepts JSON schema or natural-language format, and that context is optional for assessing overlap and discoverability. This extra information enhances parameter understanding.

    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 analyzes a single tool/function definition and returns top 3 findings on tool-call reliability. It distinguishes itself from sibling tools by focusing on individual definitions, and mentions a paid tier for auditing entire tool sets, implying this tool is for single definitions only.

    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?

    The description explicitly states when to use this tool: 'when a user shares a tool/function definition and asks why the model is calling it wrong, not calling it when expected, or confusing it with other tools.' It also provides an alternative: 'For auditing an entire tool set together, use the paid tier.'

    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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