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agentailor

SlimContext MCP Server

by agentailor
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Server Quality Checklist

67%
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  • Latest release: v0.1.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: summarize_messages uses AI-powered summarization to compress history by creating concise summaries, while trim_messages uses token-based trimming to remove oldest messages when exceeding thresholds. There is no overlap in their approaches, making them easily distinguishable.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with underscore separation: summarize_messages and trim_messages. The naming is predictable and readable, with no deviations in style or convention.

    Tool Count3/5

    With only 2 tools, the server feels thin for a context management domain, as it lacks operations like retrieval, update, or deletion of summaries/trims. However, the tools cover compression strategies adequately for a minimal scope.

    Completeness2/5

    The server is severely incomplete for context management; it only offers compression methods (summarization and trimming) but lacks any tools to retrieve, modify, or manage the compressed contexts, leaving agents with no way to access or update the results of these operations.

  • Average 4/5 across 2 of 2 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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

  • Behavior3/5

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

    With no annotations provided, the description carries full burden. It discloses key behavioral traits: the compression strategy (removes oldest non-system messages), preservation rules (preserves system messages and recent context), and triggering condition (token count exceeds threshold). However, it doesn't mention performance characteristics, error conditions, or what happens when compression isn't possible.

    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 tightly packed sentences with zero waste. The first sentence states the core purpose, the second explains the specific algorithm. Every word earns its place, and the most important information (what the tool does) is front-loaded.

    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 tool with 4 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate but not complete context. It explains the compression algorithm well but doesn't describe the return value format or error handling. The description compensates somewhat for the lack of annotations by explaining behavioral aspects.

    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 thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'token-based trimming' which relates to the parameters but doesn't explain their interactions or provide additional semantic context. Baseline 3 is appropriate when schema does the heavy lifting.

    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 verb 'compress' and resource 'chat message history' with specific strategy details ('token-based trimming', 'removes oldest non-system messages'). It distinguishes from sibling 'summarize_messages' by focusing on compression rather than summarization.

    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 for when to use this tool ('when token count exceeds threshold'), but doesn't explicitly state when NOT to use it or mention the sibling tool 'summarize_messages' as an alternative. The guidance is implicit rather than explicit.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the tool's strategy ('AI-powered summarization'), what gets preserved ('system messages and recent context'), and what gets compressed ('older messages'), but doesn't mention rate limits, authentication requirements (though API key parameter hints at this), or error conditions.

    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 perfectly concise with two sentences that each earn their place: the first states the core functionality, the second explains the preservation strategy. No wasted words, well-structured, and front-loaded with the essential purpose.

    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 complex tool with 7 parameters and no output schema, the description provides good context about the summarization approach and preservation logic. However, it doesn't explain what the output looks like (summary format) or potential limitations, leaving some gaps in completeness despite the strong schema coverage.

    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 7 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.

    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 with specific verbs ('compress', 'creates concise summaries') and resources ('chat message history'), and distinguishes it from the sibling tool 'trim_messages' by specifying it uses 'AI-powered summarization strategy' rather than simple trimming.

    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 for when to use this tool ('compress chat message history', 'creates concise summaries of older messages'), but doesn't explicitly state when NOT to use it or mention the sibling tool 'trim_messages' as an alternative for different compression needs.

    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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