Enables agents to query the Brave Search API across web, local, news, image, and video indexes through five MCP tools, returning consistent result details.

Enables agents to look up a library's Context7-compatible ID and fetch its current, version-specific documentation and code examples, optionally focused on a topic and capped at a chosen token budget.

Enables agents to list indices, read mappings, run query DSL and ES|QL searches, and inspect shards on an Elasticsearch cluster through its HTTP API.

Enables agents and terminal users to browse MongoDB databases and collections, run queries and aggregation pipelines, modify documents, manage indexes and collections, inspect statistics and logs, and explain query plans.

Enables AI agents to read and write a local-first Markdown/Obsidian notebook via a single capture call that routes text into the right notebook, board, or daily journal entry and returns related notes, alongside search, note and card management, and numeric metric series. Access is governed by per-agent tokens with read/write/admin scopes, optional folder restrictions, and an audit log of every action.

Enables agents and terminal users to inspect ClickHouse over HTTP by listing databases and tables and running guarded read-only SELECT queries without modifying data.

Enables chat agents and automation tools to query Tesla vehicle status and, when explicitly enabled, send signed vehicle commands via MCP over Streamable HTTP or local stdio.

This MCP server enables AI agents and teams to coordinate through shared inboxes, threaded conversations, attachments, expiring file reservations, prompts, and live notifications, while replaying missed events after restarts.

Exposes a read_source tool that lets agents read cited tickets, docs, and files through the same path the checker verifies, so quoted business rules match their sources and stay in sync as those sources or their authors change. It also keeps undecided product decisions explicit as open questions rather than letting agents invent answers.

Enables AI coding agents to read GitHub repositories, files, commits, issues, pull requests, releases, Actions, and search results through 18 read-only MCP tools. It reuses existing credentials, routes around unreliable networks, and caches responses on disk to stay within rate limits.

Enables AI agents to execute commands and read files or directories on pre-registered, fingerprint-verified SSH hosts by alias or IP, while keeping encrypted passwords local and inaccessible to the agent.

Enables MCP clients and AI agents to author, inspect, validate, and render PromoShot .promo projects over stdio, delegating rendering to the promo CLI.

Enables AI agents to import and refresh product feeds, edit products, write rules, run optimization and audits, connect ad channels, set performance labels, and create product ad creatives.

Checks media-plan taste claims against Qloo audience data and returns a verdict per pick with the audience's real preferences. Also exposes what fans of one thing love in another category, so agents can validate picks before spending ad money.

Give your AI agents persistent, collective memory — with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.

Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, ag

Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, ag

Multi-language repository map for agents and humans: MCP tools, dependency graphs, symbol indexes, impact review, and bounded source evidence.

A KeePass-compatible password and secrets manager for people who work with AI agents.

Lightweight Long-Term Memory for LLM Agents.

A local-first personal knowledge layer for AI agents. Build evolving knowledge spaces with LLM Wiki, knowledge graphs, and agent workflows.

Enables AI agents to obtain deterministic, structured JVM production diagnostics—including deadlocks, hot locks, bottleneck frames, and planned GC/heap analysis—from thread dumps, GC logs, and heap histograms without hallucinating from raw logs.

A structural code search engine for Al agents.

Run coding agents on machines you own. Open-source, self-hostable agent workspace.