mcp-server-qdrant
Server Quality Checklist
Latest release: v0.8.1
- Disambiguation5/5
The two tools have clearly distinct purposes: qdrant-find for retrieval and qdrant-store for storage. There is no ambiguity between them.
Naming Consistency5/5Both tool names follow a consistent pattern with the 'qdrant-' prefix followed by a clear verb ('find', 'store'), making them predictable and easy to understand.
Tool Count4/5With only 2 tools, the server is minimal but it covers the core operations of storing and retrieving memories. It could benefit from additional tools like delete, but the count is appropriate for a simple memory store.
Completeness3/5The server lacks update and delete operations, which are notable gaps for memory management. While store and find cover basic needs, agents may require more functionality for complete lifecycle management.
Average 3.1/5 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 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.
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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 provided. Description does not disclose behavioral traits such as whether it overwrites, appends, or handles duplicates. Expected side effects (creating new entries) are implied but not explicit.
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?
Single short sentence, concise and front-loaded. However, it sacrifices clarity for brevity.
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?
No output schema; description does not explain return values or success confirmation. With only a vague mention of 'memory', the tool's usage in a broader context is inadequately described.
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% with adequate parameter descriptions. The tool-level description adds no additional meaning beyond the schema, so baseline score of 3 applies.
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?
Description 'Keep the memory for later use, when you are asked to remember something' vaguely indicates a store operation but does not explicitly state that it stores text into a Qdrant collection. The sibling tool qdrant-find clarifies context, but the description itself is not precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus qdrant-find or when not to use it. No prerequisites or context for appropriate usage.
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 full responsibility for behavioral disclosure. It does not mention any behavioral traits such as read-only nature, performance considerations, or potential side effects, leaving significant gaps.
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 uses bullet points for clarity. However, there is slight redundancy among the listed use cases (e.g., find by content vs access for analysis), which could be tightened.
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?
For a simple tool with two parameters and no output schema, the description covers purpose and usage reasonably well. However, it lacks behavioral transparency and does not describe return values, which would be helpful for a complete picture.
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 already describes both parameters ('What to search for' and 'The collection to search in') with 100% coverage. The description adds no additional meaning beyond what the schema provides, so baseline score is appropriate.
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 starts with a clear verb+resource: 'Look up memories in Qdrant.' It lists specific use cases (find by content, access for analysis, get personal info), differentiating from the sibling 'qdrant-store' which likely handles 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 states when to use the tool with bullet points. While it doesn't mention when not to use it or name alternatives, the sibling 'qdrant-store' provides implicit contrast, making the usage context clear.
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 there are no obvious security issues.
- Evaluate tool definition quality.
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