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musnows

RAG MCP Tool

by musnows
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one reads raw file content, while the other searches a RAG database. There is no overlap in functionality or ambiguity about when to use each tool.

    Naming Consistency4/5

    Both tools use snake_case naming, which is consistent. However, the verb patterns differ slightly: 'read' vs. 'search', which is acceptable but not perfectly uniform. The naming is clear and follows a logical convention.

    Tool Count2/5

    With only two tools, the server feels severely under-scoped for a RAG (Retrieval-Augmented Generation) system. A typical RAG server would include tools for indexing, updating, deleting, or managing the database, not just reading files and searching. This minimal set limits functionality significantly.

    Completeness2/5

    The tool surface is highly incomplete for a RAG domain. There are no tools for indexing documents, updating the database, deleting entries, or managing the RAG system's lifecycle. This forces agents into dead ends for basic operations like adding new data or maintaining the database.

  • Average 3.1/5 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

  • 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 tool searches a RAG database but doesn't describe what 'RAG database' entails, how results are returned, whether it's read-only or has side effects, performance characteristics, or error handling. For a search tool with zero annotation coverage, this is a significant gap in transparency.

    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 and front-loaded: the first sentence states the core purpose, followed by a brief parameter explanation. There's no wasted text, but the structure could be slightly improved by integrating parameter details more seamlessly rather than a separate 'Args:' section.

    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 an output schema (which handles return values), the description doesn't need to explain outputs. However, with no annotations, 2 parameters (one optional), and 0% schema coverage, the description provides basic purpose and parameter semantics but lacks behavioral context and usage guidelines, making it minimally adequate but incomplete for effective agent use.

    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 0%, so the description must compensate. It adds meaning by explaining 'keyword' as a 'Search query' and 'dir_path' as an 'Optional directory to search in' with default behavior. This clarifies semantics beyond the bare schema, but it doesn't detail format constraints (e.g., path syntax) or search specifics (e.g., case sensitivity), leaving some gaps.

    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 tool's purpose: 'Search for keyword in RAG database.' This specifies the verb ('Search') and resource ('RAG database'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'read_raw_file' (which likely reads files rather than searching indexed content), so it misses the highest score.

    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 mentions the optional 'dir_path' parameter but doesn't explain when to specify it or when other tools might be more appropriate. There's no context about prerequisites, limitations, or comparisons with 'read_raw_file', leaving usage decisions unclear.

    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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It states the action ('Read raw content') but doesn't mention permissions required, file size limits, encoding issues, error handling, or what happens with binary vs. text files. This leaves significant behavioral gaps for a file I/O operation.

    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 extremely concise with zero wasted words. It states the purpose in one clear sentence, then provides parameter semantics in a structured format. Every sentence earns its place, and the information is front-loaded appropriately.

    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's simplicity (one parameter) and the presence of an output schema (which handles return values), the description covers the basics adequately. However, for a file reading operation with no annotations, it should ideally mention more about behavioral aspects like permissions, encoding, or error cases to be fully 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?

    The description adds meaningful context for the single parameter by specifying 'Absolute path to the file', which clarifies the expected format beyond what the schema provides (schema coverage is 0%). This compensates well for the lack of schema descriptions, though it doesn't elaborate on path validation or OS-specific considerations.

    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 ('Read') and resource ('raw content of a file'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'search_rag', which appears to serve a different function (search vs. raw reading).

    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. While 'search_rag' seems different, there's no explicit comparison or context about when raw file reading is appropriate versus searching. No prerequisites or exclusions are mentioned.

    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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