Skip to main content
Glama
kuzudb

Kuzu MCP server

Official
by kuzudb
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: getSchema retrieves database structure metadata, while query executes data retrieval/manipulation operations. There is no overlap or ambiguity between these functions, making tool selection straightforward for an agent.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (getSchema, query). While 'query' is a noun rather than verb_noun, it's a standard database term that pairs naturally with 'getSchema', maintaining clear and predictable naming throughout the minimal toolset.

    Tool Count2/5

    With only 2 tools, this server feels severely under-scoped for a database interface. A typical database MCP server would include tools for data manipulation (insert, update, delete), transaction management, connection handling, or at least parameterized queries beyond just raw query execution.

    Completeness2/5

    For a database server, the surface is significantly incomplete. While getSchema and query provide read capabilities, there are no tools for data modification (insert/update/delete), transaction control, connection management, or even parameterized query execution. This creates dead ends for agents needing to perform basic database operations.

  • Average 3.1/5 across 2 of 2 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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.

  • 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 full burden for behavioral disclosure but only states the basic function. It doesn't mention whether this is a read-only or write operation, what permissions are required, potential side effects, error handling, or any limitations like query complexity or timeout constraints.

    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?

    The description is extremely concise - a single sentence that directly states the tool's purpose with zero wasted words. It's front-loaded with the essential information and contains no unnecessary elaboration.

    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 database query tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of results to expect, error conditions, or the fact that this appears to be a general-purpose query tool versus the schema-specific sibling. The agent lacks crucial context for proper tool selection and invocation.

    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%, so the schema already documents the single 'cypher' parameter. The description doesn't add any additional meaning about parameter usage beyond what the schema provides, maintaining the baseline score for high schema coverage.

    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?

    The description clearly states the action ('Run a Cypher query') and target resource ('on the Kuzu database'), providing specific verb+resource pairing. However, it doesn't differentiate from the sibling tool 'getSchema' - both could involve querying but for different purposes.

    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?

    No guidance is provided about when to use this tool versus the sibling 'getSchema' tool, nor any context about appropriate use cases. The description simply states what the tool does without indicating when it should be selected over alternatives.

    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 states what the tool does but doesn't describe how it behaves—e.g., whether it returns a full schema, requires permissions, has side effects, or handles errors. This leaves significant gaps for a tool that interacts with a database.

    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?

    The description is a single, direct sentence with no wasted words, clearly front-loading the core purpose. It's appropriately sized for a simple tool with no parameters, making it highly efficient and easy to parse.

    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?

    Given the tool's complexity (simple, no parameters) and lack of annotations and output schema, the description is minimally adequate but incomplete. It states what the tool does but doesn't cover behavioral aspects like return format or error handling, which are important for a database tool, leaving room for improvement.

    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 tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to compensate for missing parameter info, so it meets the baseline of 4 for tools with no parameters, as it doesn't mislead about inputs.

    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?

    The description clearly states the action ('Get') and the resource ('schema of the Kuzu database'), making the purpose understandable. However, it doesn't differentiate from its sibling tool 'query' (which likely executes queries rather than retrieving metadata), so it doesn't reach the highest score.

    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 provides no guidance on when to use this tool versus alternatives like the 'query' sibling tool. It lacks context about scenarios where retrieving the schema is appropriate, such as for database exploration or query planning, leaving usage entirely implicit.

    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

kuzu-mcp-server MCP server

Copy to your README.md:

Score Badge

kuzu-mcp-server 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/kuzudb/kuzu-mcp-server'

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