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gilanggsb

knowledge-mcp

by gilanggsb

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: knowledge_search for query-based relevance, knowledge_get for retrieving by ID, and knowledge_list for enumerating with filters. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent pattern: knowledge_ + verb (search, get, list). The naming is uniform and predictable, with no mixed conventions.

    Tool Count5/5

    Three tools is a well-scoped count for a read-focused knowledge server. Each tool covers a distinct core operation (search, retrieve, list) without unnecessary bloat.

    Completeness2/5

    The server is entirely read-only, offering no operations to create, update, or delete knowledge documents. For a domain implied as 'knowledge management', this is a significant gap that would prevent agents from writing to the knowledge base.

  • Average 3.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 23 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.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

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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, the description must carry behavioral transparency, but it only states the literal action without disclosing any additional behavioral traits (e.g., read-only guarantee, pagination, auth requirements, potential cost). Listing is implicitly read-only, but the description adds no value beyond the action itself.

    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 sentence that is front-loaded with the primary action and quickly mentions optional filtering. It is concise with no wasted words.

    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?

    Without an output schema, the description should explain return values, but it doesn't. It also does not mention dependencies like repository being required for repository scope, though the schema covers that. The description is too minimal to be fully contextual for a 5-parameter tool with no output schema.

    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 baseline is 3. The description mentions 'scope and prefix' as filters, but this doesn't add meaning beyond what the schema already describes for these parameters. It doesn't enrich the parameter understanding.

    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 says 'List knowledge documents', which is a specific verb and resource. The mention of optional filtering by scope and prefix distinguishes it from sibling tools like knowledge_search (which likely searches) and knowledge_get (which likely gets a single document).

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

    Usage Guidelines3/5

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

    The description implies usage by stating it lists documents with optional filtering, but it does not explicitly compare to alternatives or state when not to use it. It lacks clear context on when to choose this over knowledge_search or knowledge_get, though the verb 'list' gives a hint.

    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, the description carries the full burden of behavioral disclosure. It claims to retrieve 'full content', but the input schema includes a max_chars parameter that can truncate output, creating a potential contradiction between what the description says and the tool's actual behavior. The description also does not mention permissions, errors, or the safety of the read 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 two sentences, front-loaded with the main purpose, and contains zero unnecessary words. Every sentence contributes meaning.

    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?

    While the tool is simple and the schema covers parameters, there is no output schema. The description only says 'full content' without explaining the return format or whether truncation can occur. It also does not clarify how this tool relates to siblings, leaving some contextual gaps for an agent.

    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 baseline is 3. The description adds context for document_id by mentioning 'by its document ID' and for section with 'specific sections', but it does not explain max_chars beyond what the schema already provides.

    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 retrieves the full content of a knowledge document by ID, using the specific verb 'get' and the resource. It also mentions the ability to retrieve specific sections, which distinguishes it from sibling tools like knowledge_search and knowledge_list.

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

    Usage Guidelines3/5

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

    The description implies usage when you have a document ID and need its content, but it does not explicitly state when to use this tool instead of knowledge_search or knowledge_list. There are no explicit exclusions or alternatives mentioned, so guidance is only implied.

    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?

    There are no annotations, so the description carries the full burden of behavioral disclosure. It mentions that it returns ranked documents, but does not explicitly state that the operation is read-only, nor does it mention any permissions, side effects, or limitations. This is a significant gap for a tool with no annotation support.

    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, consisting of two short sentences that are front-loaded with the core purpose. Every word is informative, with no fluff or repetition of the schema.

    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?

    The description adequately states the purpose and basic output, but lacks context about the result format, pagination behavior, or distinctions among scopes. Given the tool has 6 parameters and no output schema, the description should provide more guidance to the agent, making it minimally sufficient but not completely helpful.

    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%, and the description adds no additional parameter-specific meaning beyond what the schema already provides. The mention of 'query' in the description is redundant given the schema, so the baseline of 3 applies.

    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 uses a specific verb ('Search') and resource ('knowledge documents'), clearly indicating the tool's function. It distinguishes itself from siblings 'knowledge_get' and 'knowledge_list' by focusing on search and relevance ranking.

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

    Usage Guidelines3/5

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

    The description implies usage for finding relevant documents, but does not explicitly state when to use this tool vs alternatives. It lacks any mention of exclusions, prerequisites, or specific use cases such as when to use knowledge_get or knowledge_list instead.

    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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