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liiklin

Dify Knowledge MCP Server

by liiklin
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves configuration status, while the other queries a knowledge base. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with snake_case naming (get_dify_config, query_dify_knowledge). The verbs 'get' and 'query' are appropriate and distinct, maintaining a predictable naming convention throughout.

    Tool Count2/5

    With only 2 tools, the server feels thin for a knowledge base domain. It lacks essential operations like creating, updating, or deleting knowledge entries, which limits its utility and scope compared to typical MCP servers.

    Completeness2/5

    The tool surface is severely incomplete for a knowledge base server. It only allows querying and checking configuration, missing critical CRUD operations (e.g., add, update, delete knowledge) and other lifecycle management tools, which will likely cause agent failures in comprehensive workflows.

  • 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations provided, the description carries full burden for behavioral disclosure but only states the basic action without revealing important traits. It doesn't mention whether this is a read-only operation, what permissions are needed, rate limits, error conditions, or what format results are returned in. For a search tool with no annotation coverage, 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?

    The description is a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a search tool and front-loads the essential information. Every word earns its place in communicating the tool's function.

    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 has 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, how they're structured, or important behavioral aspects like authentication requirements or error handling. For a knowledge base query tool with multiple configuration parameters, more context is needed to guide effective 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?

    The description mentions 'with a search query' which aligns with the 'query' parameter, but adds no additional semantic context beyond what the schema already provides. With 100% schema description coverage and detailed parameter documentation including defaults and enums, the description doesn't compensate with extra meaning. The baseline of 3 is appropriate when the schema does the heavy lifting.

    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 ('Query') and resource ('Dify knowledge base') with the action ('with a search query'), making the purpose immediately understandable. It distinguishes from the sibling tool 'get_dify_config' by focusing on search operations rather than configuration retrieval. However, it doesn't specify what kind of knowledge base content is being queried (documents, FAQs, etc.), keeping it from a perfect 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 or in what context. There's no mention of prerequisites, limitations, or comparison with the sibling tool 'get_dify_config'. The agent must infer usage purely from the tool name and parameters without any explicit direction.

    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. While 'Get' implies a read operation, it doesn't specify whether this requires authentication, what format the configuration status returns, potential rate limits, or error conditions. The description lacks essential operational context for a tool that presumably accesses system configuration.

    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 communicates the core purpose without any wasted words. It's appropriately sized for a zero-parameter tool and front-loads the essential information.

    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 zero-parameter tool with no output schema, the description provides the basic purpose but lacks important context about what 'configuration status' includes, the format of returned data, or operational considerations. Given the complexity of configuration tools and absence of annotations/output schema, more detail would be helpful for an agent to use this 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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema already fully documents this.

    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 ('Get') and target resource ('current Dify API configuration status'), providing a specific verb+resource combination. However, it doesn't differentiate from its sibling tool 'query_dify_knowledge' which appears to serve a different purpose (knowledge querying vs configuration status).

    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 its sibling 'query_dify_knowledge' or any alternatives. It simply states what the tool does without indicating appropriate contexts, prerequisites, or exclusions.

    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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  • Evaluate tool definition quality.

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