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VKneider

Slice.js Documentation MCP

by VKneider

Server Quality Checklist

67%
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  • Latest release: v1.0.3

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: get_doc_content retrieves specific pages, get_llm_full_context fetches the entire bundle, list_docs shows available sections, and search_docs performs keyword searches. There is no overlap or ambiguity in their functions.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (e.g., get_doc_content, list_docs, search_docs). The naming is predictable and readable throughout the set.

    Tool Count4/5

    With 4 tools, the count is reasonable for a documentation server, covering core needs like listing, fetching, and searching. It might be slightly thin for advanced use cases, but it's well-scoped for basic documentation access.

    Completeness4/5

    The toolset provides good coverage for documentation retrieval: listing, searching, and fetching content (both specific and full). A minor gap is the lack of update or management tools, but this is acceptable for a read-only documentation server.

  • Average 3.4/5 across 4 of 4 tools scored. Lowest: 2.8/5.

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

    • No community issues in the last 6 months
    • 1 commit 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 are provided, so the description carries the full burden of behavioral disclosure. It mentions fetching 'full content' but lacks details on permissions required, rate limits, error handling, or whether this is a read-only operation. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

    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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose, making it easy to scan and understand quickly. Every word earns its place without unnecessary elaboration.

    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?

    Given the complexity (2 parameters, no output schema, no annotations), the description is incomplete. It doesn't cover parameter details, return values, or behavioral traits like safety or performance. For a tool that fetches content, more context on output format or limitations would be helpful to compensate for the lack of structured data.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the description must compensate for undocumented parameters. It implies 'doc_id' is used to specify pages but doesn't explain its format or that it can be a single string or array. It doesn't mention 'include_metadata' at all, leaving a key parameter undocumented. The description adds minimal value beyond the schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('fetches') and resource ('full content of specific doc page(s)'), making the purpose immediately understandable. It doesn't explicitly distinguish from sibling tools like 'list_docs' or 'search_docs', but the specificity of 'full content' versus listing or searching provides some implicit differentiation.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives like 'list_docs' or 'search_docs'. It doesn't mention prerequisites, such as needing a valid doc_id, or contextual factors like performance implications for fetching multiple pages. Usage is implied but not explicitly stated.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It states the search scope ('across all docs') but lacks critical behavioral details: it doesn't specify if results are paginated, what the return format is (e.g., list of titles or full content snippets), or any rate limits. For a search tool with zero annotation coverage, this is insufficient.

    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 a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for a simple tool, making it easy to parse quickly.

    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?

    Given the tool's moderate complexity (search with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain return values, error conditions, or behavioral constraints, leaving significant gaps for the agent to infer.

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

    Parameters2/5

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

    Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description mentions 'keyword/phrase' which hints at the 'query' parameter, but it doesn't explain 'max_results' or provide any syntax details (e.g., query operators). It adds minimal value beyond the bare schema, failing to compensate for the coverage gap.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Searches') and target resource ('across all docs') with the method ('by keyword/phrase'). It distinguishes this from siblings like 'get_doc_content' (retrieves specific content) and 'list_docs' (likely lists without search). However, it doesn't explicitly differentiate from 'get_llm_full_context', which might be a more comprehensive search, so it's not a perfect 5.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer 'search_docs' over 'list_docs' or 'get_llm_full_context', nor does it specify prerequisites like needing a query. This leaves the agent with minimal context for tool selection.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Returns') but doesn't describe what 'available' means (e.g., all sections, filtered by permissions), the return format (e.g., list of strings, structured data), or any constraints (e.g., pagination, rate limits). This leaves significant gaps for a tool with no annotation coverage.

    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 a single, efficient sentence that directly states the tool's function without any wasted words. It's front-loaded with the core action, making it easy to parse quickly.

    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?

    Given the tool has 0 parameters and no output schema, the description is minimally adequate but incomplete. It explains what the tool does at a high level but lacks details on behavior, return values, and differentiation from siblings. For a simple listing tool, this is the minimum viable, but it could benefit from more context to guide usage effectively.

    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?

    The input schema has 0 parameters with 100% description coverage, meaning no parameters need documentation. The description doesn't add parameter information, which is appropriate here. Baseline is 4 for 0 parameters, as there's nothing to compensate for, and the description doesn't introduce confusion.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb ('Returns') and resource ('available documentation sections/categories'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_doc_content' or 'search_docs', which would require mentioning it's a listing/overview function rather than retrieving content or searching.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention that this tool is for browsing categories before accessing content with 'get_doc_content' or for when 'search_docs' is more appropriate for specific queries. Without such context, usage is implied but not explicit.

    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 key traits: the output size (~2000 lines), token consumption ('consumes considerable tokens'), and the action of adding documentation to context. However, it lacks details on potential errors or rate limits, keeping it from a perfect score.

    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 front-loaded with the core purpose, followed by critical usage instructions, all in two efficient sentences with zero wasted words. Every sentence earns its place by providing 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 (no parameters, no output schema, no annotations), the description is largely complete, covering purpose, usage, and behavioral traits. However, it could be more complete by specifying the format of the returned documentation or any limitations, slightly reducing the score.

    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?

    The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description adds value by explaining the tool's behavior and implications, but it could slightly enhance semantics by mentioning any implicit assumptions, such as the source of the 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 specific action ('fetches') and resource ('complete documentation bundle'), distinguishing it from sibling tools like get_doc_content, list_docs, and search_docs by emphasizing the comprehensive nature of the retrieval.

    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?

    It provides explicit guidance on when to use this tool (to get all documentation at once) and includes a crucial prerequisite: 'Ask the user for confirmation before executing this tool,' which helps differentiate it from alternatives that might be more targeted or less resource-intensive.

    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:

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