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channico

MCP Knowledge Assistant

by channico
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    search and fetch have clearly distinct purposes: one finds documents by query, the other retrieves a specific document by ID. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools use simple, consistent single-verb names ('search' and 'fetch') that clearly indicate their actions. The pattern is uniform.

    Tool Count3/5

    With only 2 tools, the server is minimal but functionally complete for a simple search-and-retrieve pattern. It feels thin but is reasonable for a narrow purpose.

    Completeness4/5

    The two tools cover the core workflow of searching and retrieving documents. Minor gaps could include listing all documents or getting metadata, but the essential lifecycle is covered.

  • Average 3.6/5 across 2 of 2 tools scored. Lowest: 2.9/5.

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

    • No community issues in the last 6 months
    • 9 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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 the full burden of behavioral disclosure. It reveals that the tool does semantic matching over a natural-language query, but says nothing about how results are ranked, paginated, limited, scoped, or whether interactions are read-only or have side effects. It is not misleading, but it adds very little beyond what the name and schema already imply.

    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 front-loaded sentence with zero wasted words. It is appropriately brief for a minimal tool definition, though one could argue it is slightly under-specified—but the economy of expression is commendable.

    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?

    For a tool with 1 parameter, no annotations, 0% schema coverage, and an unaddressed sibling 'fetch', one sentence is insufficient context. The description would be more complete by mentioning result limits, sort order, or any behavioral differences from fetch. The presence of an output schema helps explain return values, but the in/out behavior is still too thin for an agent to fully understand the tool's scope.

    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, and it does add value by clarifying that 'query' accepts natural-language text rather than structured filters or keywords. However, it provides no examples, formatting hints, length limits, or syntax conventions—the description does the bare minimum to make the parameter actionable.

    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?

    'Find documents relevant to a natural-language query' uses a clear verb ('find'), a resource ('documents'), and a scope qualifier ('natural-language query'). It is clear and specific, but it does not explicitly distinguish itself from its sibling 'fetch', so it earns a 4 rather than a 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?

    There is no guidance on when to use search versus its sibling 'fetch', no exclusions, and no when-to-use context. The description implies usage through the term 'search', but a sibling tool with zero differentiator guidance means the agent is left without decision support. This is 'no guidance' and scores a 2.

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

  • Behavior3/5

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

    With no annotations provided, the description carries the full behavioral disclosure burden. It reveals that this returns a complete document (not a summary) and that it depends on search-generated IDs, but doesn't mention error handling, permissions, or potential side effects. For a simple read operation this is adequate but has 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?

    One sentence, no redundant details, and the key information (retrieve by ID) is front-loaded. Perfect conciseness.

    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 that an output schema is present and there is only one parameter, the description sufficiently covers the essential context and its relationship to the sibling search tool. It could be improved by mentioning error cases or what happens if the ID is invalid, but for this simplicity it is complete enough.

    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 single parameter 'id' has no schema description, so the description's phrase 'returned by search' provides crucial semantic context, explaining where the ID comes from and how it should be used to retrieve the document.

    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 the specific verb 'Retrieve' with the resource 'one complete document' and the qualifier 'using an ID returned by search', which clearly defines the tool's scope and distinguishes it from the sibling tool 'search'.

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

    Usage Guidelines4/5

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

    It explicitly states that the ID must come from search, establishing a clear workflow (search first, then fetch). It doesn't explicitly mention alternatives, but the context is clear enough for an agent to know when to use this tool versus search.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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