"Qase" matching MCP servers:

Enables AI assistants to search, retrieve, and browse Markdown documentation in a local folder, then answer questions with cited sources.

Provides search and retrieval of Hacker Public Radio episodes, transcripts, hosts, and series via MCP tools and resources, with fuzzy matching and flexible transcript search.

Enables Claude to refresh and query a deduplicated record of newly filed US securities class actions, including lead plaintiff deadlines, class periods, announcing law firms, and matched federal court dockets. Supports filtering cases by deadline window, ticker, company, or status, and inspecting how each source announcement was matched, merged, or set aside.

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

Exposes a knowledge base to LLM clients through tools, resources, and prompts.

Provides MCP tools for AI assistants to retrieve compact daily futures data packets, submit manual market data, run whitelisted crawlers, search research summaries, and save/read historical reports on demand for caustic soda, PVC, and coking coal futures.

Read-only MCP server for precise lookup, search, and versioned retrieval of OWASP testing guides (WSTG, MASTG, ISTG, AITG) with full provenance on every result.

A TypeScript-based template for rapidly developing MCP servers with modular tool architecture, built-in validation using Zod schemas, and comprehensive error handling.

Enables AI agents to share a persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from sources like Obsidian vaults, code, and transcripts through MCP and REST APIs. Provides token-optimized, tiered retrieval with full-text and semantic search so every agent session builds on accumulated context.

Enables AI agents to capture, classify, synthesize, and retrieve persistent shared knowledge from sources such as Obsidian, code, transcripts, bookmarks, and sessions. It provides token-efficient search, context briefings, and tiered memory through MCP and REST APIs.

Enables AI agents to share a persistent, self-learning memory by ingesting and classifying knowledge from Obsidian, code, transcripts, bookmarks, and terminal sessions, then retrieving token-optimized context through MCP and REST APIs.

A cross-project knowledge base MCP server supporting three-layer search (text matching, TF-IDF, and semantic vectors) with a Web UI for management.

Enables AI agents to share persistent memory by capturing, classifying, synthesizing, and retrieving knowledge through token-optimized, three-tier retrieval with hybrid keyword and semantic search. Supports bidirectional Obsidian sync, session and bug-fix capture, and a self-learning loop that compounds context across sessions.

Gives AI agents persistent, shared memory by ingesting notes, code, transcripts, and session captures, then serving token-optimized context and hybrid keyword/semantic search over MCP and REST. Agents can classify, promote, and synthesize stored material so each session builds on prior work instead of starting from zero.

Enables AI agents and clients to capture, classify, synthesize, and retrieve persistent knowledge from multiple sources through MCP and REST APIs, with tiered memory and token-optimized context.

Gives AI agents a shared persistent memory by ingesting Obsidian vaults, code, transcripts, web clips, and session or bug-fix captures, then serving token-optimized context briefings, keyword and semantic search, and cross-source synthesis. Enables multiple agents to read and write the same growing knowledge base so each session builds on the last.

Enables AI agents to search and read local Markdown notes through MCP tools for file listing, keyword matching, summaries, and section extraction, without uploading files to third-party services. It supports stdio and Streamable HTTP access with API key authentication and SQLite call monitoring.

Enables AI agents to look up an individual federal case's or defendant's outcome by docket number plus district or circuit, and to answer aggregate caseload-statistics questions such as counts of filings and terminations broken down by nature of suit, offense code, disposition, fiscal year, or district/circuit. Data comes from the Federal Judicial Center's Integrated Database, covering civil, criminal, and appellate filings that opinion/docket readers have no aggregate data for.

Provides a production-ready Model Context Protocol server with dual STDIO and Streamable HTTP transports, enabling file operations, memory, database queries, RAG, web search, GitHub integration, background tasks, and prompt-based workflows.

Enables AI agents to share persistent memory by ingesting, classifying, synthesizing, and retrieving knowledge through MCP and REST APIs, with token-optimized, tier-aware search and Obsidian sync.