"Apache Kafka" matching MCP servers:

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

Provides full-text search and retrieval tools for Apache Spark documentation using SQLite FTS5 with BM25 ranking. It enables AI assistants to efficiently search, filter by section, and read specific Spark documentation pages.

Local-first MCP server providing semantic search over library docs, fully offline. Single Go binary speaks MCP over stdio against a vector index pinned to the binary version. Like Context7 with the internet turned off. Apache 2.0. Linux + macOS, also available as a container image.

MCP server for answering questions about Apache Software Foundation policies, offering tools to list, retrieve, and search policy documents on releases, licensing, branding, security, infrastructure, and incubator topics.

Enables AI assistants to answer questions about Apache Iggy using the project's current guides and Rust API documentation, with search, retrieval, and documentation analysis tools.

Enables lookups across Apache Iceberg Javadocs, local source evidence, and Iceberg REST Catalog operations through MCP tools and prompts.

Self-hosted Go MCP server that crawls documentation sites into local Markdown/JSONL corpora and lets agents read pages, run crawls, check freshness, and diff changes with zero network access at read time. Single static binary, no CGO, Apache-2.0.

Read-only MCP server for the Apache Incubator Confluence wiki, enabling page lookup, search, and navigation through natural language.

Enables assistants to file, search, and manage invoices, contracts, policies, and official letters, and to create, explain, update, and remind on their derived deadlines. It also supports mail scanning, warranty checks, notification testing, settings changes, and upkeep through a running local paperwork app.

Extracts text and metadata from various file formats (PDF, DOCX, images with OCR) using Apache Tika, enabling AI assistants to understand file contents.