{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-neo4j-contrib-mcp-neo4j","slug":"mcp-neo4j-contrib-mcp-neo4j","name":"mcp-neo4j","type":"mcp","url":"https://github.com/neo4j-contrib/mcp-neo4j","page_url":"https://unfragile.ai/mcp-neo4j-contrib-mcp-neo4j","categories":["mcp-servers"],"tags":["database","mcp","mcp-server","neo4j","stdio"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_0","uri":"capability://tool.use.integration.cypher.query.execution.with.llm.driven.text2cypher.translation","name":"cypher query execution with llm-driven text2cypher translation","description":"Executes Cypher queries against Neo4j databases and provides Text2Cypher workflow capabilities that translate natural language prompts into executable Cypher queries using LLM reasoning. The mcp-neo4j-cypher server uses fastMCP v2.x with @mcp.tool decorators to expose query execution as MCP tools, integrating Neo4j AsyncDriver (>=5.26.0) for asynchronous database connectivity and Pydantic models for structured input validation and response formatting.","intents":["Execute Cypher queries directly against a Neo4j database from an AI agent","Convert natural language questions into Cypher queries without manual query writing","Retrieve structured graph data and return results in a format consumable by LLMs","Build agentic workflows that iteratively refine Cypher queries based on LLM feedback"],"best_for":["AI agents and LLM applications querying graph databases","Teams building semantic search and knowledge graph retrieval systems","Developers automating graph database interactions without writing Cypher manually"],"limitations":["Text2Cypher accuracy depends on LLM quality and database schema context provided in prompts","Query execution latency includes network round-trip to Neo4j plus LLM inference time for translation","No built-in query optimization or cost estimation — relies on Neo4j query planner","Requires explicit schema context or examples for LLM to generate correct Cypher syntax"],"requires":["Python 3.9+","Neo4j database instance (5.x or 6.x) with network connectivity","Neo4j driver package (>=5.26.0)","fastMCP (>=2.0.0)","LLM API key (OpenAI, Anthropic, or compatible) for Text2Cypher workflows","MCP client integration (Claude Desktop, custom agent framework, etc.)"],"input_types":["natural language queries (for Text2Cypher)","Cypher query strings (for direct execution)","structured parameters (Pydantic-validated dicts)"],"output_types":["structured JSON result sets from Cypher queries","generated Cypher query strings (from Text2Cypher)","query execution metadata (rows affected, execution time)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_1","uri":"capability://memory.knowledge.persistent.knowledge.graph.memory.for.ai.agents.with.semantic.search","name":"persistent knowledge graph memory for ai agents with semantic search","description":"Provides a Neo4j-backed memory system for AI agents that stores facts, relationships, and context as a persistent knowledge graph, enabling semantic search and retrieval across agent sessions. The mcp-neo4j-memory server implements a data model architecture that maps agent interactions into graph nodes and relationships, with search and retrieval tools that query the knowledge graph using vector embeddings or Cypher-based pattern matching to surface relevant context for LLM reasoning.","intents":["Store and retrieve agent conversation history and learned facts across multiple sessions","Build long-term memory for multi-turn agent interactions without token limit constraints","Enable semantic search over agent knowledge to find relevant context for current tasks","Maintain relationship graphs between entities (users, documents, concepts) for contextual reasoning"],"best_for":["Multi-turn AI agents requiring persistent memory across sessions","Knowledge management systems where relationships between facts matter","Applications needing semantic search over agent-learned information","Teams building RAG systems with graph-structured knowledge"],"limitations":["Memory retrieval latency depends on graph query complexity and database size","Requires explicit schema design for how agent interactions map to graph nodes/relationships","No automatic deduplication or conflict resolution for similar facts stored at different times","Vector embedding search requires external embedding model or API (not built-in)"],"requires":["Python 3.9+","Neo4j database instance with persistent storage","Neo4j AsyncDriver (>=5.26.0)","fastMCP (>=2.0.0)","Embedding model or API for semantic search (optional but recommended)","MCP client with memory persistence support"],"input_types":["agent interaction logs (text, structured events)","entity and relationship definitions (JSON/Pydantic models)","semantic search queries (natural language or embeddings)"],"output_types":["retrieved knowledge graph subsets (nodes and relationships)","ranked search results with relevance scores","formatted context for LLM consumption"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_10","uri":"capability://tool.use.integration.claude.desktop.integration.with.manifest.json.configuration","name":"claude desktop integration with manifest.json configuration","description":"Provides Claude Desktop integration through manifest.json configuration files that declare MCP server availability, transport mode, and connection parameters. Each server includes a manifest.json that specifies the server name, description, command to launch (stdio), and optional HTTP endpoint configuration. Claude Desktop reads these manifests to discover and connect to MCP servers, enabling seamless integration without manual configuration. The manifest pattern allows users to enable/disable servers and switch between local and remote deployments by editing configuration.","intents":["Enable Claude Desktop users to discover and connect to MCP servers automatically","Configure MCP server launch parameters (command, environment variables) for Claude Desktop","Switch between local (stdio) and remote (HTTP) MCP server deployments without code changes","Document MCP server capabilities and connection requirements for end users"],"best_for":["Claude Desktop users wanting to extend Claude with Neo4j capabilities","Teams distributing MCP servers to non-technical users","Organizations managing multiple MCP servers for Claude Desktop","Projects requiring easy enable/disable of MCP server features"],"limitations":["Manifest.json configuration is Claude Desktop-specific; not portable to other MCP clients","Changes to manifest.json require Claude Desktop restart to take effect","No built-in versioning or compatibility checking between manifest and server versions","Manifest configuration is stored in user's local filesystem; not centrally managed"],"requires":["Claude Desktop application (latest version)","manifest.json file in server package","Server launch command (stdio) or HTTP endpoint URL","Environment variables for server configuration (optional)"],"input_types":["manifest.json configuration file","server launch command and parameters","environment variable specifications"],"output_types":["Claude Desktop server discovery and connection","MCP tool availability in Claude Desktop UI"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_2","uri":"capability://planning.reasoning.graph.data.model.design.validation.and.visualization.with.arrows.integration","name":"graph data model design, validation, and visualization with arrows integration","description":"Enables AI agents and developers to design, validate, and visualize Neo4j graph data models through MCP tools that generate model definitions, validate schema constraints, and produce visual representations. The mcp-neo4j-data-modeling server integrates with Arrows (Neo4j's diagram tool) to export models as visualizations, uses Pydantic models for schema validation, and provides tools for Cypher generation from model definitions, allowing agents to reason about data structure and generate schema-aware queries.","intents":["Design graph data models collaboratively with AI agents suggesting schema improvements","Validate proposed data models against constraints and best practices before implementation","Generate visual diagrams of data models for documentation and stakeholder communication","Automatically generate Cypher CREATE statements and indexes from model definitions"],"best_for":["Teams designing graph schemas with AI assistance","Data architects documenting and validating complex graph models","Agents generating schema-aware Cypher queries from model context","Organizations migrating relational schemas to graph structures"],"limitations":["Model validation covers structural constraints but not domain-specific business rules","Arrows visualization export requires internet connectivity to Arrows service","No automatic schema migration or versioning — requires manual deployment","Cypher generation assumes standard Neo4j conventions; custom procedures not auto-detected"],"requires":["Python 3.9+","fastMCP (>=2.0.0)","Pydantic for model validation","Internet connectivity for Arrows visualization export","Neo4j database instance for testing generated schemas (optional)"],"input_types":["model definitions (JSON/Pydantic schemas)","node and relationship specifications","constraint and index requirements"],"output_types":["validated model definitions","Cypher DDL statements (CREATE INDEX, CREATE CONSTRAINT)","Arrows diagram URLs and SVG/PNG exports","model documentation in markdown/HTML"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_3","uri":"capability://tool.use.integration.neo4j.aura.cloud.instance.management.via.authenticated.api","name":"neo4j aura cloud instance management via authenticated api","description":"Manages Neo4j Aura cloud database instances through MCP tools that authenticate with Aura API credentials and expose instance lifecycle operations (create, delete, pause, resume, update). The mcp-neo4j-cloud-aura-api server implements authentication patterns for Aura API, uses Pydantic models for request/response validation, and provides tools for querying instance status, managing backups, and configuring instance parameters without direct database access.","intents":["Programmatically create and destroy Neo4j Aura instances for ephemeral environments","Monitor and manage instance status, scaling, and resource allocation","Automate backup and restore operations for Aura instances","Integrate Aura instance management into agent-driven infrastructure workflows"],"best_for":["DevOps teams automating Neo4j Aura infrastructure","Agents managing multi-tenant database instances","Organizations with dynamic instance provisioning requirements","Teams building self-service database platforms"],"limitations":["Requires valid Aura API credentials with appropriate permissions","Instance creation/deletion operations are asynchronous; polling required for completion status","No built-in rate limiting — relies on Aura API quotas","Cannot modify instance type or storage after creation; requires delete and recreate"],"requires":["Python 3.9+","fastMCP (>=2.0.0)","Neo4j Aura account with API credentials","Aura API key and secret (environment variables or config file)","Network connectivity to Aura API endpoints"],"input_types":["instance configuration parameters (name, region, memory, storage)","authentication credentials (API key/secret)","instance identifiers (UUID or name)"],"output_types":["instance status objects (JSON with state, connection details, metrics)","operation confirmation responses","backup/restore status and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_4","uri":"capability://tool.use.integration.mcp.server.transport.abstraction.with.stdio.http.sse.and.docker.deployment","name":"mcp server transport abstraction with stdio, http/sse, and docker deployment","description":"Provides flexible transport layer abstraction for all four MCP servers, supporting stdio (for direct process communication), HTTP with Server-Sent Events (for network access), and containerized Docker deployment. Built on Starlette middleware for HTTP transport, with CORS and TrustedHost security middleware, allowing a single MCP server implementation to be deployed across multiple transport modes without code changes. Configuration is managed through environment variables and config files, with Docker Compose templates provided for multi-server deployments.","intents":["Deploy MCP servers locally via stdio for tight integration with Claude Desktop","Expose MCP servers over HTTP/SSE for remote access and multi-client scenarios","Run MCP servers in Docker containers for cloud deployment and orchestration","Switch between transport modes without modifying server code"],"best_for":["Developers integrating MCP servers into Claude Desktop locally","Teams deploying MCP servers to cloud infrastructure (AWS, GCP, Azure)","Organizations requiring multi-client access to shared MCP server instances","DevOps teams containerizing and orchestrating MCP services"],"limitations":["Stdio transport is single-client only; HTTP/SSE required for multi-client scenarios","HTTP transport adds network latency compared to local stdio communication","CORS and TrustedHost middleware require explicit configuration for security","Docker deployment requires container orchestration knowledge (Docker Compose, Kubernetes)"],"requires":["Python 3.9+","fastMCP (>=2.0.0)","Starlette for HTTP transport","Docker and Docker Compose (for containerized deployment)","MCP client supporting stdio or HTTP/SSE transport"],"input_types":["transport configuration (stdio, http, sse)","server binding parameters (host, port, path)","security configuration (CORS origins, trusted hosts)"],"output_types":["MCP protocol messages (JSON-RPC over stdio or HTTP)","server status and health check responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_5","uri":"capability://tool.use.integration.pydantic.based.tool.input.validation.and.structured.response.formatting","name":"pydantic-based tool input validation and structured response formatting","description":"Implements type-safe MCP tool definitions using Pydantic models for input validation and structured response formatting across all four servers. Each MCP tool is decorated with @mcp.tool and uses Pydantic models to define required/optional parameters, validate types, and provide schema documentation. Responses are formatted as structured JSON objects matching Pydantic output models, ensuring LLM clients receive well-typed, validated data that can be reliably parsed and acted upon.","intents":["Ensure MCP tool inputs are validated before execution, preventing malformed requests","Provide clear schema documentation for MCP tools that LLM clients can understand","Return structured, type-safe responses that LLMs can reliably parse and use","Enable IDE autocomplete and type checking for MCP tool definitions"],"best_for":["Teams building robust MCP servers with strict input validation","Developers integrating MCP servers with type-checking LLM frameworks","Organizations requiring audit trails of validated tool inputs","Projects where response structure consistency is critical"],"limitations":["Pydantic validation adds ~5-10ms overhead per tool invocation","Complex nested models can produce verbose JSON schemas that confuse LLMs","Custom validators require explicit implementation; no auto-generation from database schema","Validation errors are returned to LLM client; requires error handling in agent logic"],"requires":["Python 3.9+","Pydantic (>=2.0)","fastMCP (>=2.0.0)","MCP client supporting JSON schema validation"],"input_types":["Pydantic model definitions (Python dataclasses with type annotations)","JSON payloads matching Pydantic schemas"],"output_types":["JSON schema documentation for MCP tools","validated input objects (Pydantic instances)","structured JSON responses matching output models"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_6","uri":"capability://tool.use.integration.neo4j.asyncdriver.integration.for.non.blocking.database.connectivity","name":"neo4j asyncdriver integration for non-blocking database connectivity","description":"Integrates Neo4j's asynchronous driver (>=5.26.0) into MCP servers to enable non-blocking database operations that don't stall the MCP event loop. The Cypher and Memory servers use AsyncDriver with async/await patterns to execute queries concurrently, allowing multiple MCP tool invocations to query the database in parallel without blocking. Connection pooling and session management are handled by the driver, with configurable connection parameters (URI, auth, encryption) passed via environment variables.","intents":["Execute multiple Neo4j queries concurrently without blocking the MCP event loop","Handle high-concurrency scenarios where multiple agents query the database simultaneously","Maintain responsive MCP server performance under database load","Leverage Neo4j driver connection pooling for efficient resource usage"],"best_for":["High-concurrency MCP deployments serving multiple agents","Applications requiring low-latency database responses","Teams building scalable MCP infrastructure with async Python","Scenarios where query latency directly impacts agent response time"],"limitations":["AsyncDriver requires async/await syntax throughout server code; not compatible with synchronous libraries","Connection pool exhaustion can occur under extreme concurrency; requires tuning pool size","Async context management adds complexity; improper cleanup can leak connections","Neo4j transaction semantics differ slightly from synchronous driver; requires careful error handling"],"requires":["Python 3.9+ with async/await support","Neo4j driver (>=5.26.0) with async support","Neo4j database instance (5.x or 6.x)","Network connectivity to Neo4j instance","Proper async context management in MCP server code"],"input_types":["Cypher query strings","query parameters (dicts)","transaction configuration (read/write mode, timeout)"],"output_types":["query result records (async iterables)","execution summary (rows affected, execution time)","error objects (Neo4j exceptions)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_7","uri":"capability://automation.workflow.configuration.management.via.environment.variables.and.config.files","name":"configuration management via environment variables and config files","description":"Provides centralized configuration management for all MCP servers through environment variables and optional config files, enabling flexible deployment across local, cloud, and containerized environments. Configuration covers database connection parameters (Neo4j URI, auth), transport settings (stdio, HTTP host/port), security options (CORS origins, TrustedHost), and server-specific settings (LLM API keys, Aura credentials). Configuration is loaded at server startup and validated using Pydantic models, with sensible defaults for development and strict requirements for production.","intents":["Configure MCP servers for different environments (dev, staging, production) without code changes","Manage sensitive credentials (API keys, database passwords) securely via environment variables","Enable Docker and cloud deployment with externalized configuration","Support configuration inheritance and override patterns for multi-server deployments"],"best_for":["Teams deploying MCP servers across multiple environments","Organizations with strict secrets management requirements","DevOps teams automating MCP server provisioning","Projects using Docker Compose or Kubernetes for orchestration"],"limitations":["Environment variable naming conventions must be documented and followed consistently","No built-in configuration versioning or rollback; requires external change management","Sensitive values in environment variables are visible in process listings; requires OS-level security","Configuration validation happens at startup; invalid configs cause server startup failure"],"requires":["Python 3.9+","Pydantic for configuration validation","Environment variable support in deployment environment","Optional: config file format support (YAML, TOML, JSON)"],"input_types":["environment variables (KEY=value format)","config files (YAML, TOML, JSON)","command-line arguments (optional)"],"output_types":["validated configuration objects (Pydantic models)","configuration documentation/schema"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_8","uri":"capability://automation.workflow.monorepo.structure.with.independent.server.versioning.and.pypi.publishing","name":"monorepo structure with independent server versioning and pypi publishing","description":"Organizes four independent MCP server implementations (mcp-neo4j-cypher, mcp-neo4j-data-modeling, mcp-neo4j-memory, mcp-neo4j-aura-manager) as separate packages within a monorepo, each with independent versioning, PyPI publication, and deployment lifecycle. Each server has its own pyproject.toml, uv.lock, and CHANGELOG, enabling teams to release and deploy servers independently without coordinating versions. Shared dependencies (fastMCP, Pydantic, Neo4j driver) are managed at the monorepo level with version constraints in each server's pyproject.toml.","intents":["Deploy individual MCP servers without requiring all four to be updated","Manage independent release cycles for servers with different maturity levels","Share common code patterns and dependencies across servers while maintaining separation","Enable teams to use only the servers they need (e.g., just Cypher without Memory)"],"best_for":["Organizations using a subset of MCP servers and wanting to avoid unnecessary dependencies","Teams with independent release schedules for different server components","Projects requiring fine-grained version control over individual servers","DevOps teams deploying servers selectively to different environments"],"limitations":["Monorepo requires coordination for shared dependency updates across servers","No automatic dependency synchronization; each server must explicitly update shared deps","Testing shared code changes requires running tests across all affected servers","Documentation must cover each server separately, increasing maintenance burden"],"requires":["Python 3.9+","uv package manager (recommended) or pip for installation","PyPI account for publishing (if publishing custom versions)","Git for monorepo management"],"input_types":["server selection (which servers to install/deploy)","version specifications (pip/uv version constraints)"],"output_types":["installed server packages","PyPI package metadata and release notes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-neo4j-contrib-mcp-neo4j__cap_9","uri":"capability://safety.moderation.security.middleware.for.http.transport.cors.and.trustedhost.validation","name":"security middleware for http transport (cors and trustedhost validation)","description":"Implements Starlette-based security middleware for HTTP/SSE transport modes, providing Cross-Origin Resource Sharing (CORS) validation and TrustedHost enforcement to prevent unauthorized access. CORS configuration allows specifying allowed origins, methods, and headers, while TrustedHost middleware validates the Host header against a whitelist of allowed domains. Configuration is managed via environment variables, with sensible defaults for development (permissive) and strict requirements for production (explicit origin/host whitelists).","intents":["Prevent cross-origin requests from untrusted clients when MCP servers are exposed over HTTP","Validate Host headers to prevent DNS rebinding attacks","Configure different security policies for development vs production deployments","Document and enforce security requirements for HTTP-exposed MCP servers"],"best_for":["Teams deploying MCP servers over HTTP to cloud infrastructure","Organizations with strict security requirements for API access","Projects requiring multi-origin access with explicit whitelisting","DevOps teams implementing defense-in-depth security patterns"],"limitations":["CORS and TrustedHost are HTTP-level protections; do not protect against network-level attacks","Misconfigured CORS can either block legitimate clients or allow unauthorized access","TrustedHost validation only checks Host header; does not prevent IP-based access","Middleware adds ~1-2ms latency per request for header validation"],"requires":["Python 3.9+","Starlette (>=0.20.0)","fastMCP (>=2.0.0) with HTTP transport support","CORS and TrustedHost configuration via environment variables"],"input_types":["HTTP request headers (Origin, Host)","CORS configuration (allowed origins, methods, headers)","TrustedHost whitelist (domain names)"],"output_types":["HTTP response headers (Access-Control-Allow-Origin, etc.)","request acceptance/rejection decisions"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Neo4j database instance (5.x or 6.x) with network connectivity","Neo4j driver package (>=5.26.0)","fastMCP (>=2.0.0)","LLM API key (OpenAI, Anthropic, or compatible) for Text2Cypher workflows","MCP client integration (Claude Desktop, custom agent framework, etc.)","Neo4j database instance with persistent storage","Neo4j AsyncDriver (>=5.26.0)","Embedding model or API for semantic search (optional but recommended)","MCP client with memory persistence support"],"failure_modes":["Text2Cypher accuracy depends on LLM quality and database schema context provided in prompts","Query execution latency includes network round-trip to Neo4j plus LLM inference time for translation","No built-in query optimization or cost estimation — relies on Neo4j query planner","Requires explicit schema context or examples for LLM to generate correct Cypher syntax","Memory retrieval latency depends on graph query complexity and database size","Requires explicit schema design for how agent interactions map to graph nodes/relationships","No automatic deduplication or conflict resolution for similar facts stored at different times","Vector embedding search requires external embedding model or API (not built-in)","Manifest.json configuration is Claude Desktop-specific; not portable to other MCP clients","Changes to manifest.json require Claude Desktop restart to take effect","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4477028124148684,"quality":0.32,"ecosystem":0.55,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-04-10T18:53:37Z"},"community":{"stars":941,"forks":243,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-neo4j-contrib-mcp-neo4j","compare_url":"https://unfragile.ai/compare?artifact=mcp-neo4j-contrib-mcp-neo4j"}},"signature":"cd2MNBfTgbYC7rrMhko0N1MokZGGMbYfBAwVzrMoWJ4Oexh3h2BQCguPVEM0DhqmemlHyuWY7Ba5lJjgTtt/Bw==","signedAt":"2026-06-20T21:48:52.945Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-neo4j-contrib-mcp-neo4j","artifact":"https://unfragile.ai/mcp-neo4j-contrib-mcp-neo4j","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-neo4j-contrib-mcp-neo4j","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}