LangExtract
FastMCP server implementation by Larsen Weigle that provides structured information extraction from unstructured text using Google's LangExtract library and Gemini models. The server maintains persistent connections and intelligent caching for optimal performance in long-running environments like Claude Code, offering tools for text extraction, URL processing, result visualization, and file operations with precise source grounding that maps every extraction to its exact location in the original text. Built with server-side credential management and optimized for Google Gemini models (gemini-2.5-flash and gemini-2.5-pro), it serves use cases across healthcare document processing, legal contract analysis, research paper extraction, and business intelligence workflows where maintaining traceability between extracted structured data and source text is critical.
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 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- maps-location
- Source
- PulseMCP
- Repository
- github.com/larsenweigle/langextract-mcp
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve LangExtract 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.