Skip to main content
Glama
bartest5

mcp-server-qdrant

by bartest5
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.8.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: qdrant-find retrieves memories while qdrant-store saves them. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow the same 'qdrant-<verb>' pattern, using lowercase snake case. The verb clearly indicates the action (find vs. store), making the naming predictable and consistent.

    Tool Count3/5

    With only two tools, the server feels minimal for a memory system. While find and store cover basic operations, the count is borderline and could easily support more operations like delete or list.

    Completeness3/5

    The tool surface covers create (store) and read (find) but lacks update and delete operations for memories. This is a notable gap for a complete lifecycle, though the core functionality works.

  • Average 3.4/5 across 2 of 2 tools scored.

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

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

  • This repository is licensed under Apache 2.0.

  • 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, the description carries the full burden of behavioral disclosure. It only states that memory is kept for later use, but does not disclose potential side effects, idempotency, permission requirements, or return behavior. This is a significant gap for a write-type tool, similar to the update_drive example.

    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 sentence with no unnecessary words, making it concise. However, it under-specifies in terms of purpose, but that is a matter of content, not conciseness. The structure is efficient and front-loaded with the core action.

    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 tool is simple with fully documented parameters and no output schema. However, the description lacks details about return values or behavior on failure/success, and the relationship to qdrant-find is not addressed. Given the simplicity, this is adequate but has clear gaps.

    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 schema covers 100% of the parameters with descriptions, so the baseline is 3. The tool description does not add any additional meaning about the parameters beyond what the schema already provides for 'information', 'collection_name', and 'metadata'.

    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 'Keep the memory for later use' conveys a storing/remembering action but is metaphorical and lacks specificity about the resource (e.g., 'store information in a qdrant collection'). It does not explicitly differentiate from the sibling tool qdrant-find, though the contrast is implied. The verb 'keep' is less precise than 'store' or 'save'.

    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?

    The phrase 'when you are asked to remember something' provides a clear context for when to use the tool. However, it does not mention alternatives or exclusion cases, so it falls short of a perfect score.

    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 the full burden of behavioral disclosure. It only says 'look up memories,' which implies a read-only operation but does not explicitly state that, nor does it mention return format, permissions, or potential side effects. This is a significant gap for a lookup tool.

    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 concise and front-loaded with the main action. The bullet list adds some redundancy (e.g., 'Find memories by their content' is nearly synonymous with 'Look up memories'), but the overall length is appropriate and no words are wasted.

    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 tool is simple (2 params, no output schema), and the description covers its main purpose and key use cases. However, it does not describe what the tool returns (since there is no output schema), and it does not clarify that it is strictly a retrieval tool (as opposed to qdrant-store). This leaves some gaps, but the description is adequate for a basic lookup tool.

    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% (both 'query' and 'collection_name' have descriptions), so the baseline is 3. The tool description itself adds no extra parameter context, but the schema already adequately explains what each parameter does.

    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 opens with a clear verb+resource construction ('Look up memories in Qdrant'), immediately distinguishing this tool from its sibling 'qdrant-store'. The bulleted use cases further reinforce the purpose by listing specific retrieval scenarios.

    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?

    The description explicitly states when to use the tool with three concrete use cases. However, it does not mention when not to use it or reference the sibling 'qdrant-store' as an alternative, so it falls short of the explicit when/when-not guidance required for a 5.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

qdrant_mcp-server-qdrant MCP server

Copy to your README.md:

Score Badge

qdrant_mcp-server-qdrant MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bartest5/qdrant_mcp-server-qdrant'

If you have feedback or need assistance with the MCP directory API, please join our Discord server