mcp-server-qdrant
OfficialServer Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: one stores memories, the other retrieves them. There is no overlap or ambiguity.
Naming Consistency5/5Both tools follow a consistent 'qdrant-verb' pattern (qdrant-find, qdrant-store), making naming predictable and easy to understand.
Tool Count3/5Only two tools for a memory server feels borderline. While it covers basic store and find operations, typical scopes have 3-15 tools, making this slightly thin.
Completeness3/5The tool set provides create (store) and read (find) operations but lacks update and delete functionalities, which are notable gaps for a complete memory management surface.
Average 3.3/5 across 2 of 2 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- 5 of 15 community issues answered or closed in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
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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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the basic action without mentioning side effects (e.g., overwriting, idempotency, permissions, or error conditions). This is insufficient for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it is not front-loaded with the essential verb and could be more efficient (e.g., 'Store information for later retrieval'). It is adequate but not exemplary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should provide more context about return values, storage behavior, and error handling. The current description is too minimal to fully inform an agent about using this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with clear descriptions for all three parameters. The description adds minimal value beyond the schema, simply restating the 'remember' concept. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description says 'Keep the memory for later use,' which implies storage but uses the vague verb 'Keep' instead of a more precise one like 'Store' or 'Save'. It distinguishes from the sibling 'qdrant-find' by implying retrieval orientation, but lacks explicit action clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes 'when you are asked to remember something,' providing a condition for use. However, it does not explicitly state when not to use it or directly compare to the sibling tool 'qdrant-find' for retrieval.
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 burden. It indicates a lookup operation but does not disclose behavioral traits such as read-only nature, side effects, or output behavior. Adequate for a simple search but missing transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and to the point, with three bullet points. It is efficient with no superfluous information, though it could be even more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 2 required parameters, no output schema, and no annotations, the description provides adequate context for a simple retrieval tool. However, it lacks details on output format and behavioral constraints, making it minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 marginal value by linking parameters to use cases (e.g., 'Find memories by their content' maps to query), but does not significantly enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Look up memories in Qdrant' and lists specific use cases, effectively communicating the tool's purpose. It distinguishes itself from the sibling 'qdrant-store' by implying retrieval versus storage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this tool when you need to:' followed by bullet points, providing clear context for when to use it. However, it does not mention when not to use it or explicitly contrast with alternatives.
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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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.
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