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Add Honcho MCP server (#158) 

* feat: add honcho fastMCP server, stubbed out start_conversation

* feat: fully functional

* feat: update mcp guide

* chore: coderabbit review

* chore: cleanup nits

* fix: improve instructions

* fix: readme clarifications

* fix: [critical] only remove duplicates within workspace+peer, make cloudflare worker mcp server work nice

* chore: update readme

* feat: add more tools
fix: remove python in favor of one typescript MCP server

* chore: refactor for DRYness

* fix: improve readme, (temporary sdk workaround) add assistant peer with observe_me: false

* chore: switch to bun, enforce usage

fix: proper assistant config, proper message formatting

* chore: fill out readme
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docs/v2/guides/mcp.mdx
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@ -7,16 +7,16 @@ sidebarTitle: 'MCP Integration'
You can let Claude use Honcho to manage its own memory in the native desktop app by using the Honcho MCP integration! Follow these steps:
1. Clone the `honcho-mcp` repo:
1. Clone the `honcho` repo:
```
git clone git@github.com:plastic-labs/honcho-mcp.git
git clone https://github.com/plastic-labs/honcho.git
```
2. Navigate into the `honcho-mcp` folder.
2. Navigate into the `mcp` folder.
```
cd honcho-mcp
cd mcp
```
3. Sync the virtual environment. This package uses [uv](https://docs.astral.sh/uv/), [install](https://docs.astral.sh/uv/#installation) if you haven't.
@ -25,34 +25,21 @@ cd honcho-mcp
uv sync
```
4. In Claude Desktop, go to the *top left Mac Toolbar* Settings > Developer and click "Edit Config"
4. Get an API key from [honcho.dev](https://app.honcho.dev)
5. Add the following (and update paths!):
5. Run FastMCP to install the Honcho MCP server in Claude Desktop:
```
{
"mcpServers": {
"Honcho": {
"command": "/path/to/uv",
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"/path/to/honcho-mcp/main.py"
]
}
}
}
uv pip install fastmcp
fastmcp install claude-desktop server.py --env-var HONCHO_API_KEY=<your-api-key>
```
<Warning>You probably will need to put the full path to the uv executable in the command field. You can get this by running `which uv` on MacOS/Linux or `where uv` on Windows.</Warning>
<Note>You can easily install Honcho MCP in claude-code or cursor by replacing `claude-desktop` in the above command with either of those values</Note>
<Warning>Make sure Claude Desktop has access to `uv` on your system. You can get the location by running `which uv` on MacOS/Linux or `where uv` on Windows.</Warning>
6. Restart the Claude Desktop app. Upon relaunch, it should start Honcho and the tools should be available!
Just note that, by default, the MCP server is set up to use the Honcho Demo server, which only persists data for 7 days. If you're using the hosted version of Honcho, copy the `.env.template` to a proper `.env` file and update the URL and API key variables accordingly.
## Project Instructions
Finally, Claude needs instructions on how to use Honcho. The Desktop app doesn't allow you to add system prompts directly, but you can create a project and paste these [instructions](https://github.com/plastic-labs/honcho-mcp/blob/main/instructions.txt) into the "Project Instructions" field.

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# Dependencies
node_modules/
npm-debug.log*
yarn-debug.log*
yarn-error.log*
# Build outputs
dist/
.wrangler/
# Environment files
.env
.env.local
.env.production
# IDE files
.vscode/
.idea/
*.swp
*.swo
# OS files
.DS_Store
Thumbs.db
# Logs
*.log

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mcp/README.md Normal file
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@ -0,0 +1,331 @@
# Honcho MCP Server
## Quickstart: Use the Hosted MCP Server
Go to https://app.honcho.dev and get an API key. Then go to Claude Desktop and navigate to custom MCP servers.
If you don't have node/bun installed you will need to do that. You can also use npm if you already have that installed. If not, Claude Desktop or Claude Code can help!
Add Honcho to your Claude desktop config. You must provide a username for Honcho to refer to you as -- preferably what you want Claude to actually call you.
```json
{
"mcpServers": {
"honcho": {
"command": "bunx",
"args": [
"mcp-remote",
"https://mcp.honcho.dev",
"--header",
"Authorization:${AUTH_HEADER}",
"--header",
"X-Honcho-User-Name:${USER_NAME}"
],
"env": {
"AUTH_HEADER": "Bearer <your-honcho-key>",
"USER_NAME": "<your-name>"
}
}
}
}
```
You may customize your assistant name and/or workspace ID. Both are optional.
```json
{
"mcpServers": {
"honcho": {
"command": "bunx",
"args": [
"mcp-remote",
"https://mcp.honcho.dev",
"--header",
"Authorization:${AUTH_HEADER}",
"--header",
"X-Honcho-User-Name:${USER_NAME}",
"--header",
"X-Honcho-Assistant-Name:${ASSISTANT_NAME}",
"--header",
"X-Honcho-Workspace-ID:${WORKSPACE_ID}"
],
"env": {
"AUTH_HEADER": "Bearer <your-honcho-key>",
"USER_NAME": "<your-name>",
"ASSISTANT_NAME": "<your-assistant-name>",
"WORKSPACE_ID": "<your-custom-workspace-id>"
}
}
}
}
```
## Available Tools
### start_conversation
Start a new conversation session with Honcho. This initializes a session for tracking conversation history and context.
**Returns:** A session ID that you must store and use for all subsequent interactions in this conversation.
### add_turn
Add a conversation turn (user and assistant messages) to the current session. This stores the conversation in Honcho for context tracking.
**Parameters:**
- `session_id`: The ID of the session to add the turn to
- `messages`: Array of message objects with `role` ("user" or "assistant") and `content`
**Example usage:**
```json
{
"session_id": "session-uuid",
"messages": [
{
"role": "user",
"content": "Hello, how are you?"
},
{
"role": "assistant",
"content": "I'm doing well, thank you!"
}
]
}
```
### get_personalization_insights
Get personalization insights from Honcho based on conversation history. This queries the user's conversation context to provide personalized responses.
**Parameters:**
- `session_id`: The ID of the session for context
- `query`: The question about the user's preferences, habits, etc.
**Example queries:**
- "What does this message reveal about the user's communication preferences?"
- "How formal or casual should I be with the user based on our history?"
- "What emotional state might the user be in right now?"
### search_workspace
Search for messages across the entire workspace.
**Parameters:**
- `query`: The search query to use
### get_workspace_metadata
Get metadata for the current workspace.
**Parameters:** None
### set_workspace_metadata
Set metadata for the current workspace.
**Parameters:**
- `metadata`: A dictionary of metadata to associate with the workspace
### create_peer
Create or get a peer with the specified ID and optional configuration.
**Parameters:**
- `peer_id`: Unique identifier for the peer
- `config`: Optional configuration dictionary for the peer
### get_peer_metadata
Get metadata for a specific peer.
**Parameters:**
- `peer_id`: The ID of the peer to get metadata for
### set_peer_metadata
Set metadata for a specific peer.
**Parameters:**
- `peer_id`: The ID of the peer to set metadata for
- `metadata`: A dictionary of metadata to associate with the peer
### search_peer_messages
Search for messages sent by a peer.
**Parameters:**
- `peer_id`: The ID of the peer to search messages for
- `query`: The search query to use
### chat
Query a peer's representation with natural language questions.
**Parameters:**
- `peer_id`: The ID of the peer to query
- `query`: The natural language question to ask
- `target_peer_id`: Optional target peer ID for local representation queries
- `session_id`: Optional session ID to scope the query to a specific session
### list_peers
Get all peers in the current workspace.
**Parameters:** None
### create_session
Create or get a session with the specified ID and optional configuration.
**Parameters:**
- `session_id`: Unique identifier for the session
- `config`: Optional configuration dictionary for the session
### get_session_metadata
Get metadata for a specific session.
**Parameters:**
- `session_id`: The ID of the session to get metadata for
### set_session_metadata
Set metadata for a specific session.
**Parameters:**
- `session_id`: The ID of the session to set metadata for
- `metadata`: A dictionary of metadata to associate with the session
### add_peers_to_session
Add peers to a session.
**Parameters:**
- `session_id`: The ID of the session to add peers to
- `peer_ids`: List of peer IDs to add to the session
### remove_peers_from_session
Remove peers from a session.
**Parameters:**
- `session_id`: The ID of the session to remove peers from
- `peer_ids`: List of peer IDs to remove from the session
### get_session_peers
Get all peer IDs in a session.
**Parameters:**
- `session_id`: The ID of the session to get peers from
### add_messages_to_session
Add messages to a session.
**Parameters:**
- `session_id`: The ID of the session to add messages to
- `messages`: List of message dictionaries with `peer_id`, `content`, and optional `metadata`
### get_session_messages
Get messages from a session with optional filtering.
**Parameters:**
- `session_id`: The ID of the session to get messages from
- `filters`: Optional dictionary of filter criteria
### get_session_context
Get optimized context for a session within a token limit.
**Parameters:**
- `session_id`: The ID of the session to get context for
- `summary`: Whether to include summary information (default: true)
- `tokens`: Maximum number of tokens to include in the context
### search_session_messages
Search for messages in a specific session.
**Parameters:**
- `session_id`: The ID of the session to search messages in
- `query`: The search query to use
### get_working_representation
Get the current working representation of a peer in a session.
**Parameters:**
- `session_id`: The ID of the session
- `peer_id`: The ID of the peer to get the working representation of
- `target_peer_id`: Optional target peer ID to get the representation of what peer_id knows about target_peer_id
### list_sessions
Get all sessions in the current workspace.
**Parameters:** None
## Contributing or Self Hosting
A Cloudflare Worker that implements the Model Context Protocol (MCP) to provide Honcho functionality as tools for AI assistants like Claude Desktop.
### Deploy MCP Worker
1. **Install dependencies:**
```bash
bun i
```
2. **Login to Cloudflare (if not already done):**
```bash
bun wrangler login
```
3. **Configure your worker name in `wrangler.toml`:**
- Update the `name` field to your desired worker name
- Update the worker names in the `[env.production]` and `[env.staging]` sections
4. **Test locally:**
```bash
bun dev
```
5. **Deploy to production:**
```bash
bun run deploy
```
### Configuration Options
You can customize the behavior using HTTP headers:
**Available Configuration:**
- `apiKey`: Your Honcho API key
- `baseUrl`: Custom Honcho API base URL (default: https://api.honcho.dev)
- `workspaceId`: Workspace ID (default: "default")
- `userName`: User identifier (default: "User")
- `assistantName`: Assistant identifier (default: "Assistant")
#### Using HTTP Headers:
Pass configuration to mcp-remote via custom headers:
```bash
bunx mcp-remote https://YOUR_WORKER_NAME.YOUR_SUBDOMAIN.workers.dev \
--header "Authorization:Bearer YOUR_HONCHO_API_KEY" \
--header "X-Honcho-Workspace-ID:my-workspace" \
--header "X-Honcho-User-Name:john" \
--header "X-Honcho-Assistant-Name:Claude" \
--header "X-Honcho-Base-URL:https://custom.honcho.dev"
```
**Supported Custom Headers:**
- `Authorization: Bearer YOUR_API_KEY` - Your Honcho API key
- `X-Honcho-Base-URL` - Custom Honcho API base URL
- `X-Honcho-Workspace-ID` - Workspace identifier
- `X-Honcho-User-Name` - User identifier
- `X-Honcho-Assistant-Name` - Assistant identifier
### Authentication
The MCP server requires a valid Honcho API key provided via the Authorization header.
### Testing
You can test the MCP server using `mcp-remote` with the local URL:
```bash
bunx mcp-remote http://localhost:8787 --header "Authorization:Bearer your-api-key"
```
### Error Handling
The server provides proper JSON-RPC 2.0 error responses:
- `-32700`: Parse error
- `-32600`: Invalid Request
- `-32601`: Method not found
- `-32602`: Invalid params
- `-32603`: Internal error
Common issues:
- **Missing API key**: Ensure you provide a valid Honcho API key via header or URL parameter
- **Invalid tool parameters**: Check that required parameters are provided and properly formatted
- **Network errors**: Verify the worker is deployed and accessible

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@ -0,0 +1,304 @@
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"": {
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}
}

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# Comprehensive Honcho MCP Integration Instructions
## What is Honcho?
Honcho is an infrastructure layer for building AI agents with social cognition and theory of mind capabilities. It enables personalized AI interactions by building coherent models of user psychology over time. The Honcho MCP server simplifies the integration to just 3 essential functions. Here's how to use them:
### Step 1: Start New Conversation (First Message Only)
When a user begins a new conversation, always call `start_conversation`:
```text
start_conversation
```
**Returns**: A session ID that you must store and use for all subsequent interactions in this conversation.
### Step 2: Get Personalized Insights (When Helpful)
Before responding to any user message, you can query for personalization insights:
```text
get_personalization_insights
session_id: [SESSION_ID_FROM_STEP_1]
query: [YOUR_THEORY_OF_MIND_QUESTION]
```
This query takes a bit of time, so it's best to only perform it when you need personalized insights. If the query can be responded to effectively using what you already know about the user, just go ahead and answer it. However, the insights endpoint is extremely perceptive. It has the capability to reveal aspects of the user's personality, historical use of the application you are operating in, and more.
**Returns**: Personalized insights about the user based on accumulated knowledge.
**Example Queries**:
- "What does this message reveal about the user's communication preferences?"
- "How formal or casual should I be with the user based on our history?"
- "What is the user really asking for beyond her explicit question?"
- "What emotional state might the user be in right now?"
- "How can I best help the user with her current request?"
### Step 3: Respond to User
Craft your response using any insights gained from Step 2.
### Step 4: Store the Conversation Turn (After Each Exchange)
**CRITICAL**: Always store both the user's message AND your response using `add_turn`:
```text
add_turn
session_id: [SESSION_ID_FROM_STEP_1]
messages: [
{
"role": "user",
"content": "[USER'S_EXACT_MESSAGE]"
},
{
"role": "assistant",
"content": "[YOUR_EXACT_RESPONSE]"
}
]
```
## Complete Example Flow
Here's exactly what to do for a new conversation:
1. **User says**: "Hi Claude! My name is Sarah and I'm feeling overwhelmed with work"
2. **Start conversation**:
```text
start_conversation
```
→ Returns: `session_abc123`
3. **Get insights** (optional but recommended):
```text
get_personalization_insights
session_id: "session_abc123"
query: "What does the user's message about feeling overwhelmed tell me about her current state and how should I respond?"
```
→ Returns insights about Sarah's emotional state and preferred communication style
4. **Respond to Sarah**: "Hi Sarah! I can hear that you're feeling overwhelmed with work right now..."
5. **Store the turn**:
```text
add_turn
session_id: "session_abc123"
messages: [
{
"role": "user",
"content": "Hi Claude! My name is Sarah and I'm feeling overwhelmed with work"
},
{
"role": "assistant",
"content": "Hi Sarah! I can hear that you're feeling overwhelmed with work right now..."
}
]
```
## Continuing an Existing Conversation
For subsequent messages in the same conversation:
1. **User says**: "Thanks for listening. Can you help me prioritize my tasks?"
2. **Respond**: "Based on our conversation, I can see you value..."
3. **Store the turn**:
```text
add_turn
session_id: "session_abc123"
messages: [
{
"role": "user",
"content": "Thanks for listening. Can you help me prioritize my tasks?"
},
{
"role": "assistant",
"content": "Based on our conversation, I can see you value..."
}
]
```
## Best Practices for Personalization Queries
Ask theory-of-mind questions that reveal:
**Communication Style**: "How formal/casual should I be?" "What does this reveal about their preferences?"
**User Needs**: "What are they really asking for?" "What emotional state are they in?"
**Relationship**: "How can I build rapport?" "What engages them most?"
**Task Approach**: "How do they prefer problem-solving?" "What detail level do they want?"
## Error Handling
- **Authorization Errors and Timeouts**: Make sure user has configured API key and URL for Honcho
- **ValueError**: Messages were incorrectly formatted, make sure to include role and content
- **"No personalization insights found"**: Normal when there's limited history with the user
- **Session management**: The MCP server handles all session persistence automatically
## Key Principles
1. **Always start with `start_conversation` for new conversations**
2. **Store every message exchange with `add_turn`**
3. **Use `get_personalization_insights` strategically for better responses**
4. **Ask thoughtful theory-of-mind questions**
5. **Never expose technical details to the user**
6. **The system maintains context automatically between sessions**

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{
"name": "honcho-mcp-proxy",
"version": "1.0.0",
"description": "Cloudflare Worker proxy for Honcho MCP Server",
"main": "worker.ts",
"packageManager": "bun@1.2.0",
"engines": {
"node": ">=18.0.0",
"bun": ">=1.2.0"
},
"scripts": {
"preinstall": "node -e \"if(process.env.npm_config_user_agent?.includes('npm')){console.error('❌ Please use bun instead of npm!\\n📦 Run: bun install\\n🌐 Install bun: https://bun.sh/');process.exit(1)}\"",
"dev": "wrangler dev",
"deploy": "wrangler deploy",
"deploy:staging": "wrangler deploy --env staging"
},
"dependencies": {
"@honcho-ai/sdk": "^1.2.1"
},
"devDependencies": {
"@cloudflare/workers-types": "^4.20241002.0",
"typescript": "^5.3.3",
"wrangler": "^4.24.3"
}
}

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{
"compilerOptions": {
"target": "ES2022",
"lib": ["ES2022"],
"module": "ES2022",
"moduleResolution": "bundler",
"allowSyntheticDefaultImports": true,
"strict": true,
"skipLibCheck": true,
"types": ["@cloudflare/workers-types"]
},
"include": ["**/*.ts"],
"exclude": ["node_modules"]
}

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name = "honcho-mcp"
main = "worker.ts"
compatibility_date = "2024-12-09"
compatibility_flags = ["nodejs_compat"]
[env.production]
name = "honcho-mcp"
[env.staging]
name = "honcho-mcp-staging"
[observability.logs]
enabled = true

4
sdks/python/pyproject.toml
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@ -30,8 +30,8 @@ classifiers = [
]
[project.urls]
Homepage = "https://github.com/plastic-labs/honcho-sdks"
Repository = "https://github.com/plastic-labs/honcho-sdks"
Homepage = "https://github.com/plastic-labs/honcho"
Repository = "https://github.com/plastic-labs/honcho"
[dependency-groups]
dev = [