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130,079 tools. Last updated 2026-05-07 00:09

"A graph database management system: Neo4j" matching MCP tools:

  • Validate any European VAT number in real-time against the official EU VIES system. Use when a user asks 'is this VAT number valid?', 'verify this company's tax ID', 'check if this EU company is registered', or needs VAT validation for invoicing, compliance, or KYC. Real-time query to the European Commission's VIES database — the same system used by tax authorities across all 27 EU member states. Returns: valid/invalid status, official registered company name, and registered address as recorded by the national tax authority. This is the definitive answer — not an estimate or cache. Note on disclosure: Spain (AEAT), Germany, and a few other EU member states do NOT disclose company name/address via VIES for privacy reasons. In those cases `status=valid` confirms the VAT is registered, but `name` and `address` return `disclosure_restricted` with a pointer to the authoritative registry (BORME for Spain).
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  • Get pre-built graph template schemas for common use cases. ⭐ USE THIS FIRST when creating a new graph project! Templates show the CORRECT graph schema format with: proper node definitions (description, flat_labels, schema with flat field definitions), relationship configurations (from, to, cardinality, data_schema), and hierarchical entity nesting. Available templates: Social Network (users, posts, follows), Knowledge Graph (topics, articles, authors), Product Catalog (products, categories, suppliers). You can use these templates directly with create_graph_project or modify them for your needs. TIP: Study these templates to understand the correct graph schema format before creating custom schemas.
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  • Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')
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  • Create a new Neo4j graph database project from a hierarchical JSON schema. ⚠️ GRAPH SCHEMA FORMAT — READ BEFORE CREATING: Graph schemas define nodes (entities) and relationships, NOT flat database tables. Each field is a dict with "type" and optional "required": true (defaults to false). SCHEMA STRUCTURE: { "nodes": { "EntityName": { "description": "What this entity represents", "flat_labels": ["AdditionalLabel"], "schema": { "field_name": {"type": "string", "required": true}, "other_field": {"type": "integer"} } } }, "relationships": { "RELATIONSHIP_TYPE": { "from": "EntityName", "to": "OtherEntity", "cardinality": "MANY_TO_MANY", "data_schema": { "field_name": {"type": "date"} } } } } FIELD TYPES: string, integer, float, boolean, date, json CARDINALITY OPTIONS: ONE_TO_ONE, ONE_TO_MANY, MANY_TO_ONE, MANY_TO_MANY HIERARCHICAL NODES: Nest entities inside parent entities to create type hierarchies. Child entities inherit parent labels automatically. Example: { "nodes": { "Animal": { "description": "Base animal entity", "flat_labels": ["LivingThing"], "schema": { "name": {"type": "string", "required": true}, "habitat": {"type": "string"} }, "Dog": { "description": "A dog (inherits Animal labels)", "flat_labels": ["Pet"], "schema": { "breed": {"type": "string", "required": true}, "trained": {"type": "boolean"} } } } }, "relationships": { "OWNS": { "from": "Person", "to": "Animal", "cardinality": "ONE_TO_MANY" } } } RULES: 1. "nodes" key is REQUIRED — must contain at least one entity 2. Each entity needs "description" and "schema" with field definitions 3. Each field is {"type": "...", "required": true/false} — required defaults to false 4. Relationship "from"/"to" must reference defined node names 5. Relationship types should be UPPER_SNAKE_CASE 6. Entity names should be PascalCase 7. Automatic fields (id, created_at, updated_at) are NOT needed 8. Use get_graph_template_schemas FIRST to see valid examples WORKFLOW: 1. Use get_graph_template_schemas to see valid examples 2. Create schema following the rules above 3. Call this tool 4. Monitor with get_job_status (2-5 min deployment) After creation, use get_job_status with returned job_id to monitor deployment.
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  • Creates a tester group for a Release Management connected app. Tester groups can be used to distribute installable artifacts to testers automatically. When a new installable artifact is available, the tester groups can either automatically or manually be notified via email. The notification email will contain a link to the installable artifact page for the artifact within Bitrise Release Management. A Release Management connected app can have multiple tester groups. Project team members of the connected app can be selected to be testers and added to the tester group. This endpoint has an elevated access level requirement. Only the owner of the related Bitrise Workspace, a workspace manager or the related project's admin can manage tester groups.
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  • Execute a SQL query on a site's database. Supports SELECT, INSERT, UPDATE, DELETE, and DDL statements. Results are limited to 1000 rows for SELECT queries. Requires: API key with write scope. Args: slug: Site identifier database: Database name query: SQL query string Returns: {"columns": ["id", "title"], "rows": [[1, "Hello"], ...], "affected_rows": 0, "query_time_ms": 12}
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Matching MCP Servers

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    An MCP server that enables LLMs to perform semantic and fulltext searches within Neo4j while executing complex, search-augmented Cypher queries for GraphRAG applications. It provides tools for database schema discovery and supports multi-provider embeddings to facilitate advanced graph traversals.
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    Enables LLMs to interact with Neo4j graph databases using natural language to execute Cypher queries and introspect database schemas. It supports both read and write operations for local, Docker, and cloud-based instances like Neo4j Aura.
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    MIT

Matching MCP Connectors

  • Calculate the recommended inverter size for running AC loads from a DC battery system. Accounts for continuous power, startup surge power (motors typically surge 2-3x), and includes a 25% headroom for the continuous rating. Returns the recommended inverter wattage and the DC current draw at system voltage.
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  • Get WordPress database information (size, tables, row counts). Requires: API key with read scope. WordPress sites only. Args: slug: Site identifier Returns: {"database": "wp_mysite", "size_mb": 45.2, "tables": 12, "total_rows": 15432}
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  • Get the name, organization, country, and description for an Autonomous System number. Lightweight version of as_whois.
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  • List Kamy's public system PDF templates. No authentication required.
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  • Rollback a graph project to a previous version. ⚠️ WARNING: This reverts schema AND code to the specified commit. Neo4j data is NOT rolled back. Use get_graph_version_history to find the commit SHA of the version you want to rollback to. After rollback, the graph API will be redeployed with the old schema.
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  • Create multiple nodes at once (up to 500 per call). Uses Neo4j UNWIND for high performance. Essential for knowledge graph population — create hundreds of entities from a single book chapter or article. Each node needs: entity_id (unique string) and data (properties dict). Example: entity_type: "concept" nodes: [ {"entity_id": "quantum-mechanics-001", "data": {"name": "Quantum Mechanics", "field": "Physics"}}, {"entity_id": "wave-function-001", "data": {"name": "Wave Function", "field": "Physics"}}, {"entity_id": "superposition-001", "data": {"name": "Superposition", "field": "Physics"}} ]
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  • List all database tables with row counts. Use before describe_table() or query().
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  • Create multiple relationships at once (up to 500 per call). Uses Neo4j UNWIND for high performance. Essential for connecting knowledge — link hundreds of concepts, people, and events in one operation. Each relationship needs: from_id, to_id, and optional data (properties). Example: rel_type: "related_to" relationships: [ {"from_id": "quantum-mechanics-001", "to_id": "wave-function-001", "data": {"strength": "strong"}}, {"from_id": "quantum-mechanics-001", "to_id": "superposition-001", "data": {"strength": "strong"}} ]
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  • Delete a file or directory from a site's container. Directories are deleted recursively. Protected system paths (e.g. /etc, /usr) cannot be deleted. Requires: API key with write scope. Args: slug: Site identifier path: Relative path to delete Returns: {"success": true, "path": "...", "message": "Deleted"} Errors: NOT_FOUND: Path doesn't exist FORBIDDEN: Protected system path
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  • Deploy a graph project to the staging environment. This triggers: (1) Schema validation, (2) Neo4j entity code generation, (3) Docker image build, (4) GitHub commit, (5) Kubernetes deployment with Neo4j instance. The operation is ASYNCHRONOUS — returns immediately with a job_id. Use get_job_status to monitor progress. Deployment typically takes 2-5 minutes. Use get_graph_project_info to verify deployment succeeded.
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  • Get the graph schema as it existed at a specific version/commit. Use get_graph_version_history to find commit SHAs. Useful for comparing schemas across versions or auditing changes.
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  • Get the deployment and version history for a graph project. Shows all schema changes with commit SHAs, timestamps, version numbers, and messages. Use this to find a specific version for rollback operations.
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  • Search 6,940 Harmonized System tariff codes. HS codes are 6-digit international product classification codes used for customs. Provide a search term or exact code.
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  • Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.
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