Neo4j GraphRAG MCP Server

Server Quality Checklist

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Latest release: v0.3.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: fulltext_search uses Lucene queries, vector_search uses embeddings, search_cypher_query combines both with graph traversal, read_neo4j_cypher handles general read queries, and get_neo4j_schema_and_indexes provides metadata. The descriptions explicitly differentiate their use cases and when to apply each, preventing confusion.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with clear verb_noun or noun_verb structures: fulltext_search, vector_search, search_cypher_query, read_neo4j_cypher, get_neo4j_schema_and_indexes. The naming is predictable and readable, making it easy for agents to infer functionality.
Tool Count5/5
With 5 tools, this server is well-scoped for Neo4j GraphRAG operations. It covers essential search methods (fulltext, vector, hybrid), general querying, and schema inspection without being overly sparse or bloated. Each tool serves a unique and necessary function in the workflow.
Completeness4/5
The toolset provides strong coverage for search and querying in a Neo4j GraphRAG context, including schema inspection, multiple search types, and flexible Cypher execution. A minor gap is the lack of write operations (e.g., create/update nodes), but this aligns with a read-focused RAG server, and agents can work around this limitation.
Average 4.3/5 across 5 of 5 tools scored. Lowest: 3.3/5.
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 MIT License.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide comprehensive behavioral hints (readOnlyHint: true, destructiveHint: false, etc.), so the bar is lower. The description adds minimal context by specifying 'read Cypher query,' which reinforces the read-only nature, but doesn't disclose additional behavioral traits like performance characteristics, result limitations, or authentication requirements.
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 directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
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 annotations provide rich behavioral hints (readOnlyHint, openWorldHint, etc.) and the schema has full coverage, the description is minimally adequate. However, without an output schema, it doesn't explain return values or result formats, and it lacks context about when to use this tool over siblings, leaving some gaps in completeness.
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?
With 100% schema description coverage, the input schema fully documents both parameters ('query' and 'params'). The description adds no additional meaning beyond what's already in the schema, so it meets the baseline score of 3 for adequate but not enhanced parameter semantics.
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 resource ('read Cypher query on the Neo4j database'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_cypher_query' or 'fulltext_search', which likely have overlapping query functionality.
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. With siblings like 'search_cypher_query' and 'fulltext_search' available, there's no indication of when this read-only query execution is preferred over other query/search tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, open-world). The description adds valuable context beyond annotations: it explains the tool's power in handling large result sets (100-1000 items), combining search with graph traversal, and using placeholders for embeddings/text. It doesn't mention rate limits or auth needs, but with annotations providing safety profile, this is sufficient.
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 well-structured with a clear purpose statement, bulleted capabilities, an example, and placeholder explanations. It's appropriately sized for a complex tool, though the bulleted list and example make it slightly longer than minimal. Every section adds value, but it could be more front-loaded with key usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no output schema) and rich annotations, the description is mostly complete. It covers purpose, usage, capabilities, and placeholders. However, it lacks details on error handling, result format, or performance considerations, which would be helpful for a tool with open-world and idempotent hints.
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 all parameters thoroughly. The description adds minimal value beyond the schema: it mentions placeholders ($vector_embedding, $fulltext_text) and provides an example, but doesn't explain parameter interactions or constraints not in the schema. Baseline 3 is appropriate when schema does the heavy lifting.
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 explicitly states the tool's purpose: 'Execute a Cypher query that uses vector and/or fulltext search indexes.' It specifies the action (execute), resource (Cypher query), and key capabilities (vector/fulltext search), clearly distinguishing it from sibling tools like read_neo4j_cypher (general queries) and vector_search/fulltext_search (standalone searches).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: for queries combining search with graph traversal, post-filtering large results, and aggregating over search results. It implicitly suggests alternatives by naming sibling tools (fulltext_search, vector_search) for simpler search-only tasks, though it doesn't explicitly say 'use X instead of Y.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide. While annotations indicate read-only, open-world, idempotent, and non-destructive operations, the description details automatic sanitization rules (large list/string handling, token limits), property selection behavior (default vs. specified properties), and the specific return format (IDs, labels, properties, scores). This goes significantly beyond the safety profile covered by annotations.
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 well-structured with clear sections (Lucene Syntax, Automatic Sanitization, Property Selection) and every sentence adds value. It's front-loaded with the core purpose, followed by essential details, and avoids redundancy. The bullet points enhance readability without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with comprehensive annotations and full schema coverage, the description provides complete context. It explains the search methodology, behavioral constraints (sanitization, limits), property handling, and references to related tools. While there's no output schema, the description clearly specifies what's returned (IDs, labels, properties, scores), making it fully adequate for agent 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?
With 100% schema description coverage, the input schema already documents all parameters thoroughly. The description adds some contextual examples (e.g., return_properties='pageNumber,id') and links parameters to other tools (fulltext_index to get_neo4j_schema_and_indexes), but doesn't provide substantial additional semantic meaning beyond what's already in the schema descriptions. This meets the baseline for high schema coverage.
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 clearly states the specific action ('Performs fulltext search') on a specific resource ('Neo4j fulltext index') using a specific method ('Lucene query syntax'). It distinguishes itself from siblings like vector_search by specifying the search type and from read_neo4j_cypher/search_cypher_query by focusing on fulltext indexing rather than general Cypher queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives. It references get_neo4j_schema_and_indexes to discover available indexes and check property warnings, and distinguishes itself from other search methods by specifying fulltext search with Lucene syntax. The context of automatic sanitization and property selection also informs appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it explains that property size warnings help avoid token limits, provides an example of how to interpret warnings, and mentions performance implications of sample_size. No contradictions with annotations exist.
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 well-structured and front-loaded with the core purpose. Each sentence adds value: the IMPORTANT note, the bulleted list of outputs, the explanation of warnings with an example, and the parameter guidance. There is no wasted text, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (schema retrieval with performance warnings), the description is complete. It covers purpose, prerequisites, outputs, parameter guidance, and integration with sibling tools. Although there is no output schema, the description adequately explains what the tool returns and how to use the information.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context: it clarifies that 'sample_size' should only be provided if requested by the user or for performance tuning, and links it to accuracy vs. speed trade-offs. This enhances understanding beyond the schema's technical description.
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 clearly states the tool's purpose: 'Returns Neo4j graph schema with search indexes and property size warnings.' It specifies the exact resources returned (schema, indexes, warnings) and distinguishes it from sibling tools by emphasizing it should be called BEFORE using search tools, establishing its unique preparatory role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage instructions: 'Call this tool BEFORE using any search tools (vector_search, fulltext_search, search_cypher_query).' It also specifies when to provide the 'sample_size' parameter ('only if requested by the user, or tuning performance'), offering clear guidance on when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations: it details automatic sanitization rules (exclusion of embedding property, handling of large lists/strings, token limits), property selection behavior (default vs. specified properties), and performance optimization (fetching extra results for kANN algorithms). Annotations cover read-only/idempotent traits, but the description enriches this with operational specifics.
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 well-structured with clear sections (Automatic Sanitization, Property Selection, Performance Optimization) and front-loaded core functionality. It's appropriately detailed for a complex tool, though slightly verbose; every sentence adds value, such as explaining sanitization rules and optimization strategies.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, rich annotations, and 100% schema coverage, the description is highly complete: it covers purpose, usage guidelines, behavioral traits, parameter effects, and references to sibling tools. No output schema exists, but the description adequately explains return values (node IDs, labels, properties, scores) and limitations.
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?
With 100% schema description coverage, the baseline is 3, but the description adds valuable semantics: it explains how return_properties affects property selection with examples, clarifies that text_query is embedded via OpenAI, and provides context on top_k optimization. However, it doesn't add syntax details beyond the schema for parameters like vector_index.
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 clearly states the tool performs vector similarity search on a Neo4j vector index, embedding text queries using OpenAI. It distinguishes from sibling tools like fulltext_search by specifying vector-based search with embedding, and from get_neo4j_schema_and_indexes by focusing on search execution rather than schema discovery.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: it references get_neo4j_schema_and_indexes to find available indexes and check property warnings, and distinguishes itself from fulltext_search by emphasizing vector-based similarity search with embedding. It also advises on performance considerations for top_k parameter usage.
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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