"Known" matching MCP servers:

Enables searching and reading AVEVA Customer Support Portal knowledge base articles, including tech notes, known issues, and error-message diagnostics, to ground AI answers in support content not found in public documentation.

Enables AI agents to query a public signed ledger of known failures and fixes before retrying, then ingest, confirm, fail, and share dense claims so subsequent agents avoid paying the same cost.

Enables AI coding agents to search and retrieve verified, reusable engineering solutions from a shared knowledge network through MCP tools.

ContractMesh is an MCP server that provides a trust-aware engineering knowledge layer, enabling agents to retrieve confirmed decisions, constraints, and known risks with provenance. It grounds AI changes in explicit engineering knowledge rather than inference alone.

A unified knowledge retrieval tool for MCP-compatible LLMs, enabling structured external knowledge access from local files and future integrations.

Provides a runtime interface that lets AI coding agents query governed Git-backed artifacts such as skills, context, and policies while reading and writing dynamic project memory—feature and decision records, observations, known issues—persisted in PostgreSQL. Supports project registration, scoped context and memory search, skill resolution, and role-gated memory promotion through API keys.