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fkiene

llmtrim

by fkiene
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Rust
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Server Quality Checklist

75%
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  • Latest release: v0.13.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one compresses an LLM request and reports token savings, the other reports cumulative savings from a local ledger. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow the same 'llmtrim_' prefix + verb pattern: 'compress' and 'stats'. Naming is consistent and predictable.

    Tool Count4/5

    With only two tools, the server is somewhat minimal but appropriate for a focused utility that provides compression and statistics. A slightly larger set might be expected for full functionality, but it is reasonable.

    Completeness3/5

    The tool set covers the core actions of compressing and viewing stats, but lacks lifecycle operations such as configuration or ledger management. This creates notable gaps for a complete workflow.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • 17 of 18 community issues answered or closed in the last 6 months
    • 631 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 Mozilla Public License 2.0.

  • 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"
  ]
}

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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?

    No annotations provided. The description does not disclose what 'recent' means, whether the tool is read-only, or any side effects. For a tool with no annotations, this leaves significant behavioral gaps.

    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 concise sentences that front-load the key information. No unnecessary words.

    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?

    For a simple reporting tool with no parameters and no output schema, the description is mostly complete. However, it omits details about the recency or time window of the data, which would help interpret results.

    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?

    Tool has zero parameters, so schema coverage is 100%. Description does not need to add parameter info, but it does not provide additional context beyond the tool's purpose.

    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 reports savings (tokens trimmed and dollars saved) from the local ledger, and references a known command for context. It distinguishes from sibling tool 'llmtrim_compress' which likely performs compression.

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

    Usage Guidelines3/5

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

    No explicit guidance on when to use this tool versus alternatives (e.g., llmtrim_compress). The description implies it is for viewing stats after compression, but does not state prerequisites or exclusions.

    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, the description fully explains the process: wraps text in minimal request, compresses, and returns shrunk text with token savings. It covers the key behavioral aspects, though it could mention whether the operation is reversible or has side effects (likely none).

    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?

    Three concise sentences with no fluff. First sentence states action and output. Second provides usage guidance. Third explains the process. Perfectly structured.

    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 tool with one parameter, the description covers what it does, when to use it, and what to expect (shrunk text with token savings). No output schema needed.

    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% and already explains the 'text' parameter. The tool description reinforces that it should be a single chunk, adding slight context but not significantly 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 clearly states it compresses a single text blob and reports token savings. It distinguishes itself from siblings by specifying 'rather than a whole request', implying the sibling tools handle multiple chunks or different scopes.

    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 says to use for shrinking one chunk (tool output, document) rather than a whole request, providing clear context. It does not mention explicit alternatives but implies the other tool for whole requests.

    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, but description discloses input/output shape, token count reporting, and per-stage breakdown. It does not mention auth or rate limits, but for a compression tool, the behavior is sufficiently 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?

    Two sentences, front-loaded with purpose, no wasted words. Every sentence provides critical information.

    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 tool with 2 parameters and no output schema, the description covers input expectations, output shape (compressed request, token counts, breakdown), and usage context, making it complete.

    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 description adds value by clarifying that 'request' accepts both JSON object and string, and that 'provider' parameter is an optional hint. It also compensates for missing output schema by describing return value structure.

    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 uses specific verb 'compress' and resource 'LLM request body', and explicitly states input and output format, distinguishing it from siblings like llmtrim_compress_text and llmtrim_stats.

    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?

    It clearly states when to use: pass raw request JSON to get compressed output and token savings. Sibling names imply alternatives, but no explicit exclusions or when-not-to-use 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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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.

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