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GongRzhe

Langflow Document Q&A Server

by GongRzhe
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JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'query_docs' has a clear and distinct purpose for querying documents, so agents cannot misselect among multiple options.

    Naming Consistency5/5

    The naming is trivially consistent as there is only one tool. It follows a verb_noun pattern ('query_docs'), which is clear and predictable, and there are no other tools to cause inconsistency or mixed conventions.

    Tool Count2/5

    The tool count is too low for a server with the apparent scope of a 'Document Q&A Server'. A single query tool feels thin and incomplete, as it lacks supporting operations like document upload, management, or retrieval, which are typical for such a domain.

    Completeness2/5

    The tool surface is significantly incomplete for a document Q&A system. While 'query_docs' allows querying, there are obvious gaps such as no tools for adding, updating, deleting, or listing documents, which are essential for a functional document management and query workflow.

  • Average 2.6/5 across 1 of 1 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

  • 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 querying a 'document Q&A system', which implies a read-only operation, but doesn't specify behavioral traits like response format, error handling, rate limits, or authentication needs. The description is too minimal to provide adequate transparency for safe and effective use.

    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 a single, clear sentence: 'Query the document Q&A system with a prompt'. It's front-loaded and efficiently conveys the core action without unnecessary words. However, it could be slightly more informative without losing conciseness, such as by specifying the system's purpose or output type.

    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 complexity (a query tool with no annotations and no output schema), the description is incomplete. It lacks details on what the tool returns, how results are formatted, any limitations, or error conditions. Without annotations or an output schema, the description should provide more context to help the agent understand the tool's behavior and outcomes, but it falls short.

    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 input schema has 100% description coverage, with the 'query' parameter documented as 'The query prompt to search for in the documents'. The description adds no additional meaning beyond this, as it doesn't elaborate on query syntax, examples, or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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

    Purpose3/5

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

    The description states the tool 'Query the document Q&A system with a prompt', which provides a basic verb ('Query') and resource ('document Q&A system'), making the purpose somewhat clear. However, it's vague about what 'document Q&A system' entails and doesn't specify the scope or type of documents, leaving room for ambiguity. Without sibling tools, it doesn't need differentiation, but the purpose could be more specific.

    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 offers no guidance on when to use this tool, such as what types of queries it supports, prerequisites, or limitations. It simply states the action without context, leaving the agent to infer usage from the tool name and parameters alone. This lack of explicit or implied guidelines reduces its helpfulness in selecting the tool appropriately.

    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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