## Summary Adds MCP `ToolAnnotations` (spec 2025-03-26+) to every tool advertised by `openhuman-core mcp`. Clients use `readOnlyHint` / `destructiveHint` / `idempotentHint` / `openWorldHint` to surface accurate safety affordances — e.g. Claude Desktop's "this tool can take destructive actions" confirmation gate. ## Problem `tools/list` today returns the curated tool surface but says nothing about each tool's behavior class. The MCP spec defines `ToolAnnotations` precisely for this — without them: - Claude Desktop / Cursor can't render confirmation gates for destructive actions. - Humans inspecting the tool list (e.g. via MCP Inspector) can't see at a glance which tools are safe to retry. - `agent.run_subagent` — the one Act-policy surface on the MCP server today — looks identical to `memory.search` to clients, even though it can call further tools (including potentially destructive ones) through any sub-agent it spawns. ## Solution - Added `annotations: Value` to `McpToolSpec` (always present, serialized into each tool's `annotations` field in the `tools/list` response). - All read-only tools share a `read_only_local_annotations()` helper (`readOnlyHint: true`, `openWorldHint: false`). - `agent.run_subagent` gets explicit annotations: `readOnlyHint: false / destructiveHint: true / idempotentHint: false / openWorldHint: true`. Sub-agent execution can call further tools, isn't a no-op on repeat, and reaches into the broader OpenHuman environment. - Updated `mcp_server/mod.rs` docstring to no longer claim the surface is purely read-only — it's curated, with `agent.run_subagent` as the one Act-policy surface. - Per spec, `destructiveHint` / `idempotentHint` are only meaningful when `readOnlyHint == false`, so the read-only helper omits them. ## Submission Checklist - [x] Tests added — `list_tools_emits_annotations_for_every_tool` asserts every entry serializes a non-null `annotations` object; a second test pins the read-only / destructive split to each tool's actual behavior. `cargo test --lib mcp_server::` runs 38/38 locally. - [x] **Diff coverage ≥ 80%** — new code is mostly literal annotations + a 6-line helper; both the "every tool has annotations" and "read-only vs destructive" axes are covered. - [x] Coverage matrix updated — `N/A: extends row 11.1.4 (MCP stdio server) rather than adding a new feature.` - [x] All affected feature IDs listed under `## Related` - [x] No new external network dependencies introduced - [x] Manual smoke checklist — `N/A: extends existing MCP surface, not a release-cut path.` - [x] Linked issue closed via `Closes #NNN` — `N/A: capability extension, no issue tracking this.` ## Impact - **Runtime**: `openhuman-core mcp` only. No HTTP RPC, web, or mobile impact. - **Compatibility**: Backward compatible — older MCP clients that don't read `annotations` ignore the field; newer clients pick up the safety affordances. - **Performance**: Negligible — one extra static `Value` per tool, serialized on each `tools/list` call (low frequency). - **Security**: No policy change. `agent.run_subagent` still goes through `enforce_act_policy`; the annotation is an advertised hint, not an enforcement point. ## Related - Feature IDs: `11.1.4` (MCP stdio server) - Spec: https://modelcontextprotocol.io/specification/2025-03-26/server/tools/#tool-annotations - Builds on: #1760, #1790, #1974 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Tools now include detailed security annotations (readOnly, destructive, idempotent, openWorld hints) so MCP clients can better understand safety and behavior. * Most tools are advertised as read-only; the subagent tool is explicitly advertised as act-capable/destructive, and one search tool is advertised as read-only but open-world. * **Tests** * Added tests ensuring each tool includes annotations and that read-only vs. destructive/open-world hints are correctly serialized. <!-- review_stack_entry_start --> [](https://app.coderabbit.ai/change-stack/tinyhumansai/openhuman/pull/2268?utm_source=github_walkthrough&utm_medium=github&utm_campaign=change_stack) <!-- review_stack_entry_end --> <!-- end of auto-generated comment: release notes by coderabbit.ai --> Co-authored-by: justin <justin80605@gmail.com> Co-authored-by: Steven Enamakel <enamakel@tinyhumans.ai>
OpenHuman
OpenHuman is your Personal AI super intelligence. Private, Simple and extremely powerful.
Discord • Reddit • X/Twitter • Docs • Follow @senamakel (Creator)
🇺🇸 English | 🇨🇳 简体中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇩🇪 Deutsch
Early Beta: Under active development. Expect rough edges.
To install or get started, either download from the website over at tinyhumans.ai/openhuman or run
# Download DMG, EXEs over at https://tinyhumans.ai/openhuman or run in from your terminal
# For macOS or Linux x64
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash
# For Windows
irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex
What is OpenHuman?
OpenHuman is an open-source agentic assistant designed to integrate with you in your daily life. Each bullet links to the deeper writeup in the docs.
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Simple, UI-first & Human A clean desktop experience and short onboarding paths take you from install to a working agent in a few clicks — no config-first setup, no terminal required. The agent has a face: a desktop mascot that speaks, reacts to its surroundings, joins your Google Meets as a real participant, remembers you across weeks, and keeps thinking in the background even when you've stopped typing.
-
118+ third-party integrations with auto-fetch: plug into Gmail, Notion, GitHub, Slack, Stripe, Calendar, Drive, Linear, Jira and the rest of your stack with one-click OAuth. Every connection is exposed to the agent as a typed tool, and every twenty minutes the core walks each active connection and pulls fresh data into the memory tree. No prompts, no polling loops you have to write, so the agent already has tomorrow's context this morning.
Managed integrations are backend-proxied through OpenHuman's Composio connector layer. If you want to run Composio directly instead of using the managed backend path, configure direct mode with your own Composio API key; real-time trigger webhooks then need to be hosted and wired by you.
-
Memory Tree + Obsidian Wiki: a local-first knowledge base built from your data and your activity. Everything you connect is canonicalized into ≤3k-token Markdown chunks, scored, and folded into hierarchical summary trees stored in SQLite on your machine. The same chunks land as
.mdfiles in an Obsidian-compatible vault you can open, browse and edit, inspired by Karpathy's obsidian-wiki workflow. -
Batteries included: web search, a web-fetch scraper, a full coder toolset (filesystem, git, lint, test, grep), and native voice (STT in, ElevenLabs TTS out, mascot lip-sync, live Google Meet agent) are wired in by default. Model routing sends each task to the right LLM (reasoning, fast, or vision) under one subscription. No "install a plugin to read files" friction. Optional local AI via Ollama for on-device workloads.
-
Smart token compression (TokenJuice): every tool call, scrape result, email body, and search payload is run through a token compression layer before it touches any LLM Model. HTML is converted to Markdown, long URLs are shortened, and verbose tool output is deduped and summarized via a configurable rule overlay etc... CJK, emoji, and other multi-byte text are preserved grapheme-by-grapheme — never stripped. You get the same information but at a fraction of the tokens. Reducing cost & latency by up to 80%.
-
Messaging channels and privacy & security: inbound/outbound across the channels you already use, with workflow data that stays on device, encrypted locally, treated as yours.
Contributing from source
New contributor? Start with CONTRIBUTING.md for the fork/PR workflow and local validation commands, or use the copy-paste AI-agent prompt in CONTRIBUTING-BEGINNERS.md. The short path is:
- Install Git, Node.js 24+, pnpm 10.10.0, Rust 1.93.0 (
rustfmt+clippy), CMake, Ninja, ripgrep, and the platform desktop build prerequisites. - Fork and clone the repo, then run
git submodule update --init --recursivebeforepnpm installso the vendored Tauri/CEF sources are present. - Use
pnpm devfor web-only UI work,pnpm --filter openhuman-app dev:appfor the desktop shell, and focused checks such aspnpm typecheck,pnpm format:check, andcargo check -p openhuman --libbefore opening a PR.
Deeper docs: Architecture · Getting Set Up · Cloud Deploy.
Context in minutes, not weeks
OpenHuman is the first agent harness that gets to know you in minutes. Inspired by Karpathy's LLM Knowledgebase. Most agents start cold. Hermes learns by watching you work; OpenClaw waits for plugins to ferry context in. Either way, you spend days or weeks before the agent knows enough about your stack to be genuinely useful.
OpenHuman summarizes and compresses all your documents, emails & chats; and creates a memory graph that lets your agent remember everything about you.
OpenHuman skips the wait. Connect your accounts, let auto-fetch pull data locally on a 20-minute loop, and then have Memory Trees compress everything into Markdown files stored intelligently in a Karpathy-style Obsidian wiki.
In just one sync pass, the agent has full (compressed) context of your inbox, your calendar, your repos, your docs, your messages. No training period. No "give it a few weeks.". It becomes you, controlled by you.
Already self-host agentmemory across other coding agents? OpenHuman ships an optional Memory backend that proxies to it — set memory.backend = "agentmemory" in config.toml and the same durable store powers OpenHuman alongside Claude Code, Cursor, Codex, and OpenCode. See the agentmemory backend page for setup.
OpenHuman vs Other Agent Harnesses
High-level comparison (products evolve, so verify against each vendor). OpenHuman is built to minimize vendor sprawl, keep workflow knowledge on-device, and give the agent a persistent memory of your data, not only chat.
| Claude Cowork | OpenClaw | Hermes Agent | OpenHuman | |
|---|---|---|---|---|
| Open-source | 🚫 Proprietary | ✅ MIT | ✅ MIT | ✅ GNU |
| Simple to start | ✅ Desktop + CLI | ⚠️ Terminal-first | ⚠️ Terminal-first | ✅ Clean UI, minutes |
| Cost | ⚠️ Sub + add-ons | ⚠️ BYO models | ⚠️ BYO models | ✅ One sub + TokenJuice |
| Memory | ✅ Chat-scoped | ⚠️ Plugin-reliant | ✅ Self-learning | 🚀 Memory Tree + Obsidian vault, optional agentmemory backend |
| Integrations | ⚠️ Few connectors | ⚠️ BYO | ⚠️ BYO | 🚀 118+ via OAuth |
| Auto-fetch | 🚫 None | 🚫 None | 🚫 None | ✅ 20-min sync into memory |
| API sprawl | 🚫 Extra keys | 🚫 BYOK | 🚫 Multi-vendor | ✅ One account |
| Model routing | 🚫 Single model | ⚠️ Manual | ⚠️ Manual | ✅ Built-in |
| Native tools | ✅ Code-only | ✅ Code-only | ✅ Code-only | ✅ Code + search + scraper + voice |
Star us on GitHub
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Contributors Hall of Fame
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