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ZeroDB MCP Server v2.0.8
Enterprise-grade Model Context Protocol (MCP) server providing full access to ZeroDB's vector search, quantum compression, NoSQL operations, and persistent memory for AI agents.
Key Features
- 60 Complete Operations - Full API coverage across all ZeroDB capabilities
- Vector Search - Semantic similarity search with 1536-dimensional embeddings
- Quantum Compression - Advanced vector compression using quantum algorithms
- NoSQL Tables - Flexible table operations for structured data
- File Storage - Secure file upload, download, and management
- Event System - Event-driven architecture with pub/sub support
- Project Management - Multi-tenant project isolation and management
- RLHF Integration - Reinforcement Learning from Human Feedback collection
- Admin Tools - System monitoring, optimization, and health checks
- Enterprise Security - JWT authentication with automatic token renewal
- 90%+ Test Coverage - Comprehensive test suite for production reliability
Table of Contents
- Installation
- Quick Start
- API Reference
- Configuration
- Examples
- Migration Guide
- Troubleshooting
- Contributing
- License
Installation
NPM Global Installation
npm install -g ainative-zerodb-mcp-server
NPX (No Installation Required)
npx ainative-zerodb-mcp-server
Requirements
- Node.js >= 18.0.0
- NPM >= 9.0.0
- ZeroDB account with API credentials (see below)
Getting Started
Step 1: Create Your ZeroDB Account
Before using the MCP server, you need a ZeroDB account and project.
Option A: Register via Web (Recommended)
Visit: https://api.ainative.studio/docs
- Click on "Register User" endpoint
- Use the "Try it out" feature
- Enter your email, password, and username
Option B: Register via API
curl -X POST 'https://api.ainative.studio/v1/public/auth/register' \ -H 'Content-Type: application/json' \ -d '{ "email": "your-email@example.com", "password": "YourSecurePassword123!", "username": "yourname" }'
Response:
{ "email": "your-email@example.com", "id": "user-uuid-here", "username": "yourname" }
Step 2: Create a Project
After registration, create a project to get your PROJECT_ID:
# 1. Login to get your access token curl -X POST 'https://api.ainative.studio/v1/public/auth/login-json' \ -H 'Content-Type: application/json' \ -d '{ "username": "your-email@example.com", "password": "YourSecurePassword123!" }'
Response:
{ "access_token": "eyJhbGc...", "token_type": "bearer", "expires_in": 1800 }
# 2. Create a project using your token curl -X POST 'https://api.ainative.studio/v1/public/projects' \ -H 'Authorization: Bearer YOUR_ACCESS_TOKEN_HERE' \ -H 'Content-Type: application/json' \ -d '{ "name": "My ZeroDB MCP Project", "description": "Project for Claude Desktop MCP integration" }'
Response:
{ "id": "550e8400-e29b-41d4-a716-446655440000", "name": "My ZeroDB MCP Project", "status": "ACTIVE", ... }
Save this Project ID! You'll need it for the MCP configuration.
Quick Start
Step 3: Configure Claude Desktop
Add to your Claude Desktop configuration file:
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{ "mcpServers": { "zerodb": { "command": "npx", "args": ["ainative-zerodb-mcp-server"], "env": { "ZERODB_API_URL": "https://api.ainative.studio", "ZERODB_PROJECT_ID": "your-project-id-here", "ZERODB_USERNAME": "your-email@example.com", "ZERODB_PASSWORD": "your-password", "MCP_CONTEXT_WINDOW": "8192", "MCP_RETENTION_DAYS": "30" } } } }
Step 4: Restart Claude Desktop
After saving the configuration, restart Claude Desktop to activate the MCP server.
Step 5: Test Your First Operation
In Claude Desktop, try:
Store a memory: "This is my first ZeroDB memory entry"
Claude will use the zerodb_store_memory tool to persist this information.
API Reference
All 60 operations are organized into 9 categories. Each operation is exposed as an MCP tool that Claude can invoke.
Memory Operations
1. zerodb_store_memory
Store agent memory in ZeroDB for persistent context across sessions.
Parameters:
content(string, required) - Memory content to storerole(string, required) - Message role:"user","assistant", or"system"session_id(string, optional) - Session identifier (auto-generated if not provided)agent_id(string, optional) - Agent identifier (auto-generated if not provided)metadata(object, optional) - Additional metadata
Returns:
{ "memory_id": "uuid", "created_at": "2025-10-14T12:00:00Z" }
Example:
{ "content": "User prefers dark mode and TypeScript", "role": "system", "metadata": { "category": "preferences" } }
2. zerodb_search_memory
Search agent memory using semantic similarity.
Parameters:
query(string, required) - Search querysession_id(string, optional) - Filter by sessionagent_id(string, optional) - Filter by agentrole(string, optional) - Filter by rolelimit(number, optional) - Max results (default: 10)
Returns:
{ "memories": [ { "memory_id": "uuid", "content": "string", "role": "user|assistant|system", "created_at": "timestamp", "similarity_score": 0.95 } ] }
Example:
{ "query": "What were the user's preferences?", "limit": 5 }
3. zerodb_get_context
Get agent context window for current session, optimized for token limits.
Parameters:
session_id(string, required) - Session identifieragent_id(string, optional) - Agent identifiermax_tokens(number, optional) - Max tokens in context (default: 8192)
Returns:
{ "session_id": "uuid", "agent_id": "uuid", "total_tokens": 1024, "memory_count": 15, "messages": [ { "role": "user", "content": "string", "timestamp": "2025-10-14T12:00:00Z" } ] }
Example:
{ "session_id": "session-123", "max_tokens": 4096 }
Vector Operations
4. zerodb_store_vector
Store vector embedding with metadata (exactly 1536 dimensions).
Parameters:
vector_embedding(array[number], required) - 1536-dimensional vectordocument(string, required) - Source document textmetadata(object, optional) - Document metadatanamespace(string, optional) - Vector namespace (default: "windsurf")
Returns:
{ "vector_id": "uuid", "namespace": "windsurf" }
Example:
{ "vector_embedding": [0.1, 0.2, ..., 0.9], // 1536 values "document": "This is the source text", "metadata": { "source": "documentation", "page": 42 } }
5. zerodb_batch_upsert_vectors
Batch upsert multiple vectors for improved performance.
Parameters:
vectors(array, required) - Array of vector objectsvector_embedding(array[number], required) - 1536-dimensional vectordocument(string, required) - Source documentmetadata(object, optional) - Document metadata
namespace(string, optional) - Vector namespace
Returns:
{ "success_count": 100, "failed_count": 0, "vector_ids": ["uuid1", "uuid2", ...] }
Example:
{ "vectors": [ { "vector_embedding": [...], "document": "Document 1", "metadata": {"index": 1} }, { "vector_embedding": [...], "document": "Document 2", "metadata": {"index": 2} } ], "namespace": "knowledge-base" }
6. zerodb_search_vectors
Search vectors using semantic similarity.
Parameters:
query_vector(array[number], required) - 1536-dimensional query vectornamespace(string, optional) - Vector namespacelimit(number, optional) - Max results (default: 10)threshold(number, optional) - Similarity threshold 0-1 (default: 0.7)
Returns:
{ "vectors": [ { "vector_id": "uuid", "document": "string", "similarity_score": 0.95, "metadata": {} } ] }
Example:
{ "query_vector": [...], // 1536 values "namespace": "windsurf", "limit": 20, "threshold": 0.8 }
7. zerodb_delete_vector
Delete a specific vector by ID.
Parameters:
vector_id(string, required) - Vector UUID to delete
Returns:
{ "success": true, "deleted_id": "uuid" }
Example:
{ "vector_id": "123e4567-e89b-12d3-a456-426614174000" }
8. zerodb_get_vector
Retrieve a specific vector by ID.
Parameters:
vector_id(string, required) - Vector UUID to retrieve
Returns:
{ "vector_id": "uuid", "vector_embedding": [...], "document": "string", "metadata": {}, "created_at": "timestamp" }
Example:
{ "vector_id": "123e4567-e89b-12d3-a456-426614174000" }
9. zerodb_list_vectors
List vectors with pagination and filtering.
Parameters:
namespace(string, optional) - Filter by namespacelimit(number, optional) - Results per page (default: 50)offset(number, optional) - Pagination offset (default: 0)
Returns:
{ "vectors": [...], "total_count": 1000, "limit": 50, "offset": 0 }
Example:
{ "namespace": "windsurf", "limit": 100, "offset": 200 }
10. zerodb_vector_stats
Get statistics about vector storage.
Parameters:
namespace(string, optional) - Filter by namespace
Returns:
{ "total_vectors": 10000, "namespaces": { "windsurf": 5000, "default": 5000 }, "storage_bytes": 1048576, "avg_dimension": 1536 }
Example:
{ "namespace": "windsurf" }
11. zerodb_create_vector_index
Create a vector search index for improved performance.
Parameters:
namespace(string, required) - Namespace to indexindex_type(string, optional) - Index type: "ivfflat" or "hnsw" (default: "hnsw")params(object, optional) - Index-specific parameters
Returns:
{ "index_id": "uuid", "namespace": "windsurf", "index_type": "hnsw", "status": "created" }
Example:
{ "namespace": "windsurf", "index_type": "hnsw", "params": { "m": 16, "ef_construction": 200 } }
12. zerodb_optimize_vectors
Optimize vector storage and indexes.
Parameters:
namespace(string, optional) - Namespace to optimize
Returns:
{ "optimized": true, "space_saved_bytes": 102400, "duration_ms": 1500 }
Example:
{ "namespace": "windsurf" }
13. zerodb_export_vectors
Export vectors to external format.
Parameters:
namespace(string, required) - Namespace to exportformat(string, optional) - Export format: "json" or "csv" (default: "json")include_embeddings(boolean, optional) - Include vector data (default: false)
Returns:
{ "export_id": "uuid", "download_url": "https://...", "expires_at": "timestamp" }
Example:
{ "namespace": "windsurf", "format": "json", "include_embeddings": true }
Quantum Operations
14. zerodb_quantum_compress
Compress vector using quantum algorithms for reduced storage.
Parameters:
vector_embedding(array[number], required) - 1536-dimensional vector to compresscompression_ratio(number, optional) - Target ratio 0-1 (default: 0.5)algorithm(string, optional) - Algorithm: "qaoa" or "vqe" (default: "qaoa")
Returns:
{ "compressed_vector": [...], "original_dimensions": 1536, "compressed_dimensions": 768, "compression_ratio": 0.5, "fidelity_score": 0.98 }
Example:
{ "vector_embedding": [...], "compression_ratio": 0.6, "algorithm": "qaoa" }
15. zerodb_quantum_decompress
Decompress quantum-compressed vector.
Parameters:
compressed_vector(array[number], required) - Compressed vector dataoriginal_dimensions(number, required) - Original dimension count
Returns:
{ "decompressed_vector": [...], "dimensions": 1536, "reconstruction_error": 0.02 }
Example:
{ "compressed_vector": [...], "original_dimensions": 1536 }
16. zerodb_quantum_hybrid_search
Perform hybrid search using quantum-enhanced similarity.
Parameters:
query_vector(array[number], required) - 1536-dimensional querynamespace(string, optional) - Vector namespacelimit(number, optional) - Max results (default: 10)quantum_weight(number, optional) - Quantum influence 0-1 (default: 0.5)
Returns:
{ "vectors": [ { "vector_id": "uuid", "document": "string", "similarity_score": 0.96, "quantum_score": 0.94, "hybrid_score": 0.95 } ] }
Example:
{ "query_vector": [...], "namespace": "windsurf", "limit": 10, "quantum_weight": 0.7 }
17. zerodb_quantum_optimize
Optimize vector space using quantum optimization.
Parameters:
namespace(string, required) - Namespace to optimizeoptimization_target(string, optional) - "storage" or "search_speed" (default: "storage")
Returns:
{ "optimized": true, "improvement_percentage": 35.5, "duration_ms": 5000 }
Example:
{ "namespace": "windsurf", "optimization_target": "search_speed" }
18. zerodb_quantum_feature_map
Generate quantum feature map for vector.
Parameters:
vector_embedding(array[number], required) - 1536-dimensional vectorfeature_map_type(string, optional) - "zz" or "pauli" (default: "zz")
Returns:
{ "feature_map": [...], "quantum_circuit_depth": 10, "qubit_count": 12 }
Example:
{ "vector_embedding": [...], "feature_map_type": "pauli" }
19. zerodb_quantum_kernel
Compute quantum kernel similarity between vectors.
Parameters:
vector_a(array[number], required) - First 1536-dimensional vectorvector_b(array[number], required) - Second 1536-dimensional vectorkernel_type(string, optional) - "linear" or "rbf" (default: "rbf")
Returns:
{ "kernel_similarity": 0.92, "quantum_advantage": 0.15, "computation_time_ms": 45 }
Example:
{ "vector_a": [...], "vector_b": [...], "kernel_type": "rbf" }
Table/NoSQL Operations
20. zerodb_create_table
Create a new NoSQL table with schema.
Parameters:
table_name(string, required) - Table nameschema(object, required) - JSON schema definitiondescription(string, optional) - Table description
Returns:
{ "table_id": "uuid", "table_name": "string", "created_at": "timestamp" }
Example:
{ "table_name": "user_profiles", "schema": { "user_id": "uuid", "name": "string", "email": "string", "created_at": "timestamp" }, "description": "User profile information" }
21. zerodb_list_tables
List all tables in project.
Parameters:
limit(number, optional) - Results per page (default: 50)offset(number, optional) - Pagination offset (default: 0)
Returns:
{ "tables": [ { "table_id": "uuid", "table_name": "string", "row_count": 1000, "created_at": "timestamp" } ], "total_count": 10 }
Example:
{ "limit": 100, "offset": 0 }
22. zerodb_get_table
Get table details and schema.
Parameters:
table_id(string, required) - Table UUID
Returns:
{ "table_id": "uuid", "table_name": "string", "schema": {}, "row_count": 1000, "created_at": "timestamp" }
Example:
{ "table_id": "123e4567-e89b-12d3-a456-426614174000" }
23. zerodb_delete_table
Delete a table and all its data.
Parameters:
table_id(string, required) - Table UUID to delete
Returns:
{ "success": true, "deleted_id": "uuid", "rows_deleted": 1000 }
Example:
{ "table_id": "123e4567-e89b-12d3-a456-426614174000" }
24. zerodb_insert_rows
Insert rows into table.
Parameters:
table_id(string, required) - Table UUIDrows(array, required) - Array of row objects
Returns:
{ "inserted_count": 100, "row_ids": ["uuid1", "uuid2", ...] }
Example:
{ "table_id": "123e4567-e89b-12d3-a456-426614174000", "rows": [ { "user_id": "user-1", "name": "John Doe", "email": "john@example.com" }, { "user_id": "user-2", "name": "Jane Smith", "email": "jane@example.com" } ] }
25. zerodb_query_rows
Query table rows with filters.
Parameters:
table_id(string, required) - Table UUIDfilters(object, optional) - Query filterslimit(number, optional) - Max results (default: 50)offset(number, optional) - Pagination offset (default: 0)
Returns:
{ "rows": [...], "total_count": 1000, "limit": 50, "offset": 0 }
Example:
{ "table_id": "123e4567-e89b-12d3-a456-426614174000", "filters": { "email": { "$contains": "@example.com" } }, "limit": 100 }
26. zerodb_update_rows
Update rows in table.
Parameters:
table_id(string, required) - Table UUIDfilters(object, required) - Row selection filtersupdates(object, required) - Fields to update
Returns:
{ "updated_count": 50, "row_ids": ["uuid1", "uuid2", ...] }
Example:
{ "table_id": "123e4567-e89b-12d3-a456-426614174000", "filters": { "user_id": "user-1" }, "updates": { "email": "newemail@example.com" } }
27. zerodb_delete_rows
Delete rows from table.
Parameters:
table_id(string, required) - Table UUIDfilters(object, required) - Row selection filters
Returns:
{ "deleted_count": 25, "row_ids": ["uuid1", "uuid2", ...] }
Example:
{ "table_id": "123e4567-e89b-12d3-a456-426614174000", "filters": { "created_at": { "$lt": "2025-01-01" } } }
File Operations
28. zerodb_upload_file
Upload file to ZeroDB storage.
Parameters:
file_name(string, required) - File namefile_data(string, required) - Base64-encoded file datacontent_type(string, optional) - MIME typemetadata(object, optional) - File metadata
Returns:
{ "file_id": "uuid", "file_name": "string", "size_bytes": 1024, "upload_url": "string" }
Example:
{ "file_name": "document.pdf", "file_data": "base64encodeddata...", "content_type": "application/pdf", "metadata": { "category": "documentation" } }
29. zerodb_download_file
Download file from ZeroDB storage.
Parameters:
file_id(string, required) - File UUID
Returns:
{ "file_id": "uuid", "file_name": "string", "file_data": "base64encodeddata...", "content_type": "string", "size_bytes": 1024 }
Example:
{ "file_id": "123e4567-e89b-12d3-a456-426614174000" }
30. zerodb_list_files
List files with pagination.
Parameters:
limit(number, optional) - Results per page (default: 50)offset(number, optional) - Pagination offset (default: 0)content_type(string, optional) - Filter by MIME type
Returns:
{ "files": [ { "file_id": "uuid", "file_name": "string", "size_bytes": 1024, "content_type": "string", "created_at": "timestamp" } ], "total_count": 100 }
Example:
{ "limit": 100, "content_type": "application/pdf" }
31. zerodb_delete_file
Delete file from storage.
Parameters:
file_id(string, required) - File UUID to delete
Returns:
{ "success": true, "deleted_id": "uuid" }
Example:
{ "file_id": "123e4567-e89b-12d3-a456-426614174000" }
32. zerodb_get_file_metadata
Get file metadata without downloading.
Parameters:
file_id(string, required) - File UUID
Returns:
{ "file_id": "uuid", "file_name": "string", "size_bytes": 1024, "content_type": "string", "metadata": {}, "created_at": "timestamp" }
Example:
{ "file_id": "123e4567-e89b-12d3-a456-426614174000" }
33. zerodb_generate_presigned_url
Generate temporary download URL.
Parameters:
file_id(string, required) - File UUIDexpiry_seconds(number, optional) - URL validity (default: 3600)
Returns:
{ "file_id": "uuid", "presigned_url": "https://...", "expires_at": "timestamp" }
Example:
{ "file_id": "123e4567-e89b-12d3-a456-426614174000", "expiry_seconds": 7200 }
Event Operations
34. zerodb_create_event
Create a new event in the event system.
Parameters:
event_type(string, required) - Event type identifierevent_data(object, required) - Event payloadmetadata(object, optional) - Event metadata
Returns:
{ "event_id": "uuid", "event_type": "string", "created_at": "timestamp" }
Example:
{ "event_type": "user.signup", "event_data": { "user_id": "user-123", "email": "user@example.com" }, "metadata": { "source": "web" } }
35. zerodb_list_events
List events with filtering and pagination.
Parameters:
event_type(string, optional) - Filter by event typestart_date(string, optional) - Filter by start date (ISO 8601)end_date(string, optional) - Filter by end date (ISO 8601)limit(number, optional) - Results per page (default: 50)offset(number, optional) - Pagination offset (default: 0)
Returns:
{ "events": [ { "event_id": "uuid", "event_type": "string", "event_data": {}, "created_at": "timestamp" } ], "total_count": 1000 }
Example:
{ "event_type": "user.signup", "start_date": "2025-10-01T00:00:00Z", "limit": 100 }
36. zerodb_get_event
Get specific event by ID.
Parameters:
event_id(string, required) - Event UUID
Returns:
{ "event_id": "uuid", "event_type": "string", "event_data": {}, "metadata": {}, "created_at": "timestamp" }
Example:
{ "event_id": "123e4567-e89b-12d3-a456-426614174000" }
37. zerodb_subscribe_events
Subscribe to event stream (WebSocket).
Parameters:
event_types(array[string], required) - Event types to subscribe tofilters(object, optional) - Additional filters
Returns:
{ "subscription_id": "uuid", "event_types": ["user.signup", "user.login"], "websocket_url": "wss://..." }
Example:
{ "event_types": ["user.signup", "user.login"], "filters": { "source": "web" } }
38. zerodb_event_stats
Get event statistics and analytics.
Parameters:
event_type(string, optional) - Filter by event typestart_date(string, optional) - Start date (ISO 8601)end_date(string, optional) - End date (ISO 8601)group_by(string, optional) - Group by: "hour", "day", "week" (default: "day")
Returns:
{ "total_events": 10000, "event_types": { "user.signup": 1000, "user.login": 9000 }, "timeline": [ { "date": "2025-10-14", "count": 500 } ] }
Example:
{ "start_date": "2025-10-01T00:00:00Z", "end_date": "2025-10-14T23:59:59Z", "group_by": "day" }
Project Operations
39. zerodb_create_project
Create a new ZeroDB project.
Parameters:
project_name(string, required) - Project namedescription(string, optional) - Project descriptionsettings(object, optional) - Project settings
Returns:
{ "project_id": "uuid", "project_name": "string", "created_at": "timestamp" }
Example:
{ "project_name": "My AI Application", "description": "Vector search for customer support", "settings": { "retention_days": 90 } }
40. zerodb_get_project
Get project details.
Parameters:
project_id(string, required) - Project UUID
Returns:
{ "project_id": "uuid", "project_name": "string", "description": "string", "settings": {}, "created_at": "timestamp" }
Example:
{ "project_id": "123e4567-e89b-12d3-a456-426614174000" }
41. zerodb_list_projects
List all accessible projects.
Parameters:
limit(number, optional) - Results per page (default: 50)offset(number, optional) - Pagination offset (default: 0)
Returns:
{ "projects": [ { "project_id": "uuid", "project_name": "string", "created_at": "timestamp" } ], "total_count": 10 }
Example:
{ "limit": 100 }
42. zerodb_update_project
Update project settings.
Parameters:
project_id(string, required) - Project UUIDproject_name(string, optional) - New project namedescription(string, optional) - New descriptionsettings(object, optional) - Updated settings
Returns:
{ "project_id": "uuid", "updated_fields": ["project_name", "settings"], "updated_at": "timestamp" }
Example:
{ "project_id": "123e4567-e89b-12d3-a456-426614174000", "settings": { "retention_days": 120 } }
43. zerodb_delete_project
Delete a project and all its data.
Parameters:
project_id(string, required) - Project UUID to deleteconfirm(boolean, required) - Must be true to confirm deletion
Returns:
{ "success": true, "deleted_id": "uuid", "deleted_at": "timestamp" }
Example:
{ "project_id": "123e4567-e89b-12d3-a456-426614174000", "confirm": true }
44. zerodb_get_project_stats
Get project usage statistics.
Parameters:
project_id(string, required) - Project UUIDstart_date(string, optional) - Start date (ISO 8601)end_date(string, optional) - End date (ISO 8601)
Returns:
{ "project_id": "uuid", "vector_count": 10000, "memory_count": 5000, "table_count": 10, "file_count": 100, "storage_bytes": 10485760, "api_calls": 50000 }
Example:
{ "project_id": "123e4567-e89b-12d3-a456-426614174000", "start_date": "2025-10-01T00:00:00Z" }
45. zerodb_enable_database
Enable database features for project.
Parameters:
project_id(string, required) - Project UUIDfeatures(array[string], required) - Features to enable: ["vectors", "tables", "files", "events"]
Returns:
{ "project_id": "uuid", "enabled_features": ["vectors", "tables"], "enabled_at": "timestamp" }
Example:
{ "project_id": "123e4567-e89b-12d3-a456-426614174000", "features": ["vectors", "tables", "files"] }
RLHF Operations
46. zerodb_rlhf_interaction
Collect user interaction for RLHF training.
Parameters:
session_id(string, required) - Session identifieruser_input(string, required) - User's inputagent_response(string, required) - Agent's responsefeedback_score(number, optional) - User rating 1-5metadata(object, optional) - Additional context
Returns:
{ "interaction_id": "uuid", "collected_at": "timestamp" }
Example:
{ "session_id": "session-123", "user_input": "How do I reset my password?", "agent_response": "You can reset your password by...", "feedback_score": 5, "metadata": { "model": "claude-3-sonnet", "response_time_ms": 1200 } }
47. zerodb_rlhf_agent_feedback
Collect feedback about agent performance.
Parameters:
agent_id(string, required) - Agent identifiersession_id(string, required) - Session identifierfeedback_type(string, required) - "positive" or "negative"feedback_text(string, optional) - Detailed feedbackmetrics(object, optional) - Performance metrics
Returns:
{ "feedback_id": "uuid", "collected_at": "timestamp" }
Example:
{ "agent_id": "agent-123", "session_id": "session-123", "feedback_type": "positive", "feedback_text": "Very helpful and accurate responses", "metrics": { "accuracy": 0.95, "helpfulness": 0.9 } }
48. zerodb_rlhf_workflow
Collect feedback about workflow completion.
Parameters:
workflow_id(string, required) - Workflow identifiersession_id(string, required) - Session identifiercompleted(boolean, required) - Workflow completion statusduration_ms(number, optional) - Workflow durationfeedback(object, optional) - Workflow feedback
Returns:
{ "workflow_feedback_id": "uuid", "collected_at": "timestamp" }
Example:
{ "workflow_id": "workflow-123", "session_id": "session-123", "completed": true, "duration_ms": 5000, "feedback": { "ease_of_use": 5, "effectiveness": 4 } }
49. zerodb_rlhf_error
Collect error reports for model improvement.
Parameters:
session_id(string, required) - Session identifiererror_type(string, required) - Error categoryerror_message(string, required) - Error descriptioncontext(object, optional) - Error contextuser_impact(string, optional) - Impact level: "low", "medium", "high"
Returns:
{ "error_report_id": "uuid", "collected_at": "timestamp" }
Example:
{ "session_id": "session-123", "error_type": "hallucination", "error_message": "Agent provided incorrect information about product pricing", "context": { "user_query": "What is the price?", "agent_response": "The price is $50" }, "user_impact": "high" }
50. zerodb_rlhf_status
Get RLHF collection status.
Parameters:
session_id(string, optional) - Filter by sessionstart_date(string, optional) - Start date (ISO 8601)end_date(string, optional) - End date (ISO 8601)
Returns:
{ "total_interactions": 1000, "avg_feedback_score": 4.2, "positive_feedback": 850, "negative_feedback": 150, "collection_rate": 0.85 }
Example:
{ "start_date": "2025-10-01T00:00:00Z", "end_date": "2025-10-14T23:59:59Z" }
51. zerodb_rlhf_summary
Get RLHF analytics summary.
Parameters:
group_by(string, optional) - Group by: "agent", "workflow", "session" (default: "agent")start_date(string, optional) - Start date (ISO 8601)end_date(string, optional) - End date (ISO 8601)
Returns:
{ "summary": [ { "group_id": "agent-123", "interaction_count": 500, "avg_score": 4.5, "improvement_trend": 0.15 } ] }
Example:
{ "group_by": "agent", "start_date": "2025-10-01T00:00:00Z" }
52. zerodb_rlhf_start
Start RLHF collection for session.
Parameters:
session_id(string, required) - Session i
服务配置
[{'mcpServers': {'zerodb': {'args': ['ainative-zerodb-mcp-server'], 'command': 'npx', 'env': {'MCP_CONTEXT_WINDOW': '8192', 'MCP_RETENTION_DAYS': '30', 'ZERODB_API_URL': 'https://api.ainative.studio', 'ZERODB_PASSWORD': 'your-password', 'ZERODB_PROJECT_ID': 'your-project-id-here', 'ZERODB_USERNAME': 'your-email@example.com'}}}}]