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Aeolun

Repomix MCP Server

by Aeolun

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: repomix-estimate is for estimating output size before retrieval, while repomix is for actually packing repository files. There is no overlap or ambiguity between them, as one is a preparatory check and the other is the main action.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern with the 'repomix' prefix and descriptive suffixes ('-estimate' vs. no suffix for the base tool). This creates a clear hierarchy and makes the relationship between the tools immediately apparent.

    Tool Count3/5

    With only 2 tools, the server feels thin for a repository management domain. While the tools cover a specific workflow (estimation and packing), typical repository operations like listing, searching, or updating files are missing, making the scope narrow and potentially limiting for agents.

    Completeness2/5

    The server is severely incomplete for repository management. It only supports packing and estimating repository files, with no tools for creating, reading, updating, or deleting repository content, nor for common operations like cloning, branching, or committing. This will cause significant agent failures in broader repository tasks.

  • Average 4.3/5 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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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates that this tool can process both local directories and remote repositories, emphasizes the importance of filtering to avoid large outputs, and provides practical guidance on file selection patterns. However, it doesn't mention potential rate limits, authentication requirements, or error handling scenarios.

    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?

    The description is efficiently structured with purpose first, then usage context, then critical parameter guidance. Each sentence serves a distinct purpose, though the final sentence about repomix-estimate could be integrated more smoothly. Overall, it's appropriately sized and front-loaded with essential information.

    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's complexity (6 parameters, no output schema, no annotations), the description does a good job covering usage context, sibling relationships, and critical parameter guidance. It explains the tool's role in a workflow and provides practical filtering advice. The main gap is lack of output format explanation, which would be helpful since there's no output schema.

    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?

    With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description adds some value by providing concrete examples for the 'include' parameter and emphasizing its importance, but doesn't significantly enhance understanding beyond what's already in the schema. This meets the baseline expectation when schema coverage is complete.

    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 specific action ('Pack repository files into a single AI-friendly file') and resource ('repository files'), distinguishing it from its sibling repomix-estimate by emphasizing this is the actual packing tool rather than a size estimation tool. The purpose is unambiguous and actionable.

    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 provides explicit guidance on when to use this tool ('Use at session start to load context efficiently') and when to use an alternative ('Always use repomix-estimate first to check size'). It also specifies important prerequisites and sequencing, making it clear how to integrate this tool into a workflow.

    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 of behavioral disclosure. It effectively describes the tool's purpose (estimation before retrieval), provides practical guidance on parameter usage, and mentions the token threshold (<50K) for decision-making. However, it doesn't cover potential errors, rate limits, or authentication needs, leaving some behavioral aspects unspecified.

    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?

    The description is appropriately sized with three sentences that each serve a clear purpose: stating the tool's purpose, providing usage instructions, and explaining the workflow. It's front-loaded with the core purpose and avoids unnecessary repetition. However, the second sentence is somewhat long and could be more streamlined.

    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's complexity (6 parameters, no output schema, no annotations), the description provides good contextual coverage. It explains the purpose, usage workflow, and parameter importance. However, it doesn't describe what the estimation output looks like (token count, file count, etc.), which would be helpful since there's no output schema. The guidance on when to use the sibling tool partially compensates for this gap.

    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 description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds value by emphasizing the importance of the 'include' parameter with specific examples and warnings ('Always specify to avoid large outputs!'), but doesn't provide additional semantic context beyond what's in the schema for other parameters. This meets the baseline for high schema 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's purpose: 'Estimate repomix output size before retrieval.' It specifies the verb ('estimate'), resource ('repomix output size'), and distinguishes it from its sibling 'repomix' by emphasizing it's for estimation before actual retrieval. This provides specific differentiation from the sibling tool.

    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 provides explicit guidance on when to use this tool: 'ALWAYS use this first with the "include" parameter to filter only relevant files.' It also specifies when to proceed with the sibling tool: 'If estimated tokens are reasonable (<50K), proceed with repomix using the same filters.' This offers clear alternatives and conditions for tool selection.

    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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