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Tobarrientos2

Neo4j MCP Server

Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose: executing Cypher queries against a Neo4j database.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'neo4j-query' follows a clear and descriptive pattern, though no pattern can be established or broken with a single tool.

    Tool Count2/5

    A single tool is too few for a database server's apparent scope, which typically requires operations like creating, updating, deleting, and querying data. This minimal set limits functionality and forces all operations through a generic query interface, which is insufficient for structured interactions.

    Completeness2/5

    The tool surface is severely incomplete for a Neo4j database server. While 'neo4j-query' allows executing arbitrary Cypher queries, it lacks dedicated tools for common operations like creating nodes, updating relationships, or managing transactions, leaving significant gaps that agents must work around with raw queries.

  • Average 2.9/5 across 1 of 1 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under ISC License.

  • This repository includes a README.md file.

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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 carries full burden for behavioral disclosure. While 'Execute' implies a write operation, the description doesn't clarify whether this tool can perform read-only queries, mutations, or both. It lacks information about permissions required, transaction handling, result formats, or potential side effects like data modification or performance impacts.

    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, efficient sentence that communicates the core functionality without unnecessary words. It's appropriately sized for a straightforward tool and is front-loaded with the essential information. Every word earns its place in this minimal description.

    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 execution tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of results to expect, error handling, security considerations, or whether queries are read-only or can modify data. The combination of a powerful database tool with minimal description creates significant gaps in understanding.

    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 both parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. This meets the baseline expectation when schema coverage is complete, but doesn't provide extra value like Cypher syntax examples or parameter format guidance.

    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 ('Execute') and target resource ('Cypher query against the Neo4j database'), making the purpose immediately understandable. It lacks sibling differentiation, but since there are no sibling tools on this server, this doesn't reduce clarity. The description avoids tautology by specifying what type of query and database are involved.

    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, prerequisites, or limitations. It simply states what the tool does without context about appropriate use cases. With no sibling tools, the need for differentiation is reduced, but general usage context is still missing.

    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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  • Evaluate tool definition quality.

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