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famtong8-dev

w3-mcp-server-qdrant

by famtong8-dev
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.7

  • Disambiguation5/5

    The two tools have clearly distinct purposes: listing collections vs. searching within a collection. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent 'qdrant_verb_noun' pattern (list_collections, search), making it predictable.

    Tool Count3/5

    Only two tools for a vector database feels thin; typical usage would benefit from additional CRUD operations. However, for a focused query-only server, it might be borderline acceptable.

    Completeness2/5

    Significant gaps: no tools to create/delete collections, insert/update/delete points. Agent cannot populate or manage data, only list and search existing content.

  • Average 4.2/5 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
    • Last stable release on
    • 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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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.

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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

  • Behavior4/5

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

    Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds that it returns a formatted list and includes error messages (connection errors). This adds useful behavioral context beyond annotations without contradictions.

    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 well-structured with clear sections (Args, Returns, Errors) but includes some redundancy (e.g., explaining the param that is already in schema). It is not overly long, but could be slightly more concise.

    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 the low complexity of the tool, the description adequately covers return values (formatted list with metadata) and error conditions. Annotations and output schema (present) cover safety and structure. The description is complete for a simple read-only list operation.

    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% for the top-level params, but the schema does describe the response_format field with enums. The description repeats this information and adds 'Validated parameters', which provides minor additional meaning. Baseline is 3 due to schema having some descriptions, and the description adds limited value.

    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 'List all collections in Qdrant' and specifies the output includes 'point counts and vector dimensions'. It is a specific verb+resource combination, and the sibling tool qdrant_search is different (search vs list).

    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 does not provide guidance on when to use this tool versus the sibling qdrant_search. There is no mention of prerequisites, when not to use, or alternatives. Usage context is implied but not explicitly stated.

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

  • Behavior5/5

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

    Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds significant behavioral context: uses Ollama for embedding, Qdrant for search, lists specific error messages (collection not found, embedding failed, connection error), and details advanced processing steps like RRF and LLM reranking.

    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?

    Well-structured with clear sections (general description, advanced features, Args, Returns, Errors) and front-loaded purpose. While somewhat verbose, each sentence adds necessary context for a complex tool.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Comprehensively covers all aspects: purpose, all parameters (including advanced ones), return format (formatted string with similarity scores), and specific error cases. No gaps given the tool's complexity.

    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?

    Schema descriptions cover all parameters, but description adds value by explaining advanced behavior (e.g., 'auto-embedded', 'generates hypothetical document'), default values, and constraints like 'max 100 results', which goes beyond the basic schema descriptions.

    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?

    Description clearly states 'Search for similar documents in Qdrant' with specific verb 'search' and resource 'Qdrant vector database'. Distinguishes from sibling qdrant_list_collections by focusing on searching within a collection.

    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?

    Provides detailed usage context including advanced features like query expansion, HyDE, and reranking. Though no explicit when-not-to-use, the single sibling makes intent clear: this is for searching, not listing collections.

    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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