#temporal

Temporal MCP is a bridge connecting AI assistants like Claude with the powerful Temporal workflow engine. By implementing the Model Context Protocol (MCP), it allows AI assistants to discover, execute, and monitor complex workflow orchestrations—all through natural language conversations.

he purpose of this repo is to demonstrate how easy it is to leverage workflows as tools for MCP servers by taking modelcontext.io's weather example and making it durable by implementing the MCP tools as Temporal workflows

Empowering AI with Workflow Orchestration

Local-first MCP code intelligence server with 37 tools — hybrid search, blast-radius analysis, diff-aware risk-scored review, and bi-temporal memory pinned to git SHAs. Stops AI agents from hallucinating function names that don't exist in your codebase. Published 60-task retrieval benchmark with reproducible harness. MIT.

Audit-grade memory backbone for agent teams. Bi-temporal facts (event time + transaction time, with recall(as_of=...) replay), 6-step deterministic retrieval (no LLM in the critical path), conversation ingest with speaker-locked dual-pass extraction, per-tenant Postgres row-level security, and Ed25519-signed provenance. Postgres + pgvector + Neo4j defaults; ArangoDB / DynamoDB / Cosmos / AlloyDB also supported. Listed on the official MCP Registry as io.github.bolnet/attestor.