DataBridge
This MCP server implementation provides a bridge to DataBridge, enabling AI assistants to ingest and retrieve information from a local database. Developed as part of the databridge-mcp project, it offers two main tools: one for ingesting user observations with metadata, and another for retrieving relevant information based on user queries. The server uses FastMCP for efficient request handling and is designed to work with Python 3.11+. It's particularly useful for AI applications requiring persistent storage and retrieval of contextual information, supporting use cases like maintaining conversation history or building knowledge bases from user interactions. The implementation focuses on simplicity and ease of integration, making it suitable for both development and production environments.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/morphik-org/morphik-mcp
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve DataBridge from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.