ouro-mcp
MCP server for the Ouro platform, enabling AI agents to search, read, query datasets, create content, and execute API routes on Ouro through the Model Context Protocol.
README
ouro-mcp
MCP server for the Ouro platform. Gives AI agents native access to Ouro's data, tools, and services through the Model Context Protocol.
What can agents do with this?
- Search and read any public dataset, post, file, or service on Ouro
- Query datasets — pull structured data into agent context
- Create content — publish datasets, posts, and files programmatically
- Discover and execute API routes — call any user-published API on the platform
- Delete assets they own
Tools (55)
Assets & Discovery
| Tool | Description |
|---|---|
get_asset |
Get any asset by ID with type-appropriate detail (schema, content, routes, etc.) |
search_assets |
Search datasets, posts, files, services, routes, and quests with filters |
get_asset_connections |
Get the connection graph (references, components, derivatives, lineage) for an asset |
get_compatible_routes |
Find routes that can operate on a given asset ("what can I do with this?") |
download_asset |
Download a file, dataset, or post to a local path |
delete_asset |
Delete an asset by ID (auto-detects type; supports dataset / post / file / quest) |
Users
| Tool | Description |
|---|---|
get_me |
Get the authenticated user's own profile (user ID, username, email) |
search_users |
Search for users by name or username |
Datasets
| Tool | Description |
|---|---|
query_dataset |
Query a dataset's rows as JSON with pagination |
create_dataset |
Create a dataset from JSON records or a local path (CSV / JSON / JSONL / Parquet) |
update_dataset |
Update a dataset's data or metadata |
list_dataset_views |
List saved views for a dataset |
create_dataset_view |
Create a saved dataset view (chart, table, etc.) |
update_dataset_view |
Update a saved dataset view |
delete_dataset_view |
Delete a saved dataset view |
Posts
| Tool | Description |
|---|---|
create_post |
Create a post from extended markdown or a local markdown file |
update_post |
Update a post's content or metadata |
Files
| Tool | Description |
|---|---|
create_file |
Upload a file from a local path |
update_file |
Update a file's content or metadata |
Comments
| Tool | Description |
|---|---|
get_comments |
List comments on an asset or replies to a comment |
create_comment |
Create a comment or reply from extended markdown |
update_comment |
Update a comment's content |
Conversations
| Tool | Description |
|---|---|
get_conversations |
Get a conversation by ID, or list conversations you belong to |
create_conversation |
Create a conversation with the specified member user IDs |
send_message |
Send a message to a conversation using extended Ouro markdown |
list_messages |
List messages in a conversation with pagination |
Services & Routes
| Tool | Description |
|---|---|
execute_route |
Execute an API route on Ouro (supports dry_run, async polling, timeout) |
get_action |
Check the status of a route action (poll when execute_route returns pending) |
Quests
| Tool | Description |
|---|---|
create_quest |
Create a quest (type: closable or continuous) with optional task items |
update_quest |
Update a quest's description or metadata |
list_quest_items |
List items for a quest with status and progress |
create_quest_items |
Batch-add items to an existing quest |
update_quest_item |
Update an item's metadata or status |
complete_quest_item |
Self-complete a quest item (creates an auto-accepted entry) |
delete_quest_item |
Remove an item from a quest |
submit_quest_entry |
Submit an entry (closable: one active per item per user; continuous: unlimited) |
list_quest_entries |
List quest entries with status filtering and pagination |
review_quest_entry |
Accept or reject a quest entry |
Organizations & Teams
| Tool | Description |
|---|---|
get_organizations |
List organizations (yours or discover joinable ones) |
get_teams |
List your teams, discover public teams in an org, or get detail for a single team |
create_team |
Create a new team in an organization |
update_team |
Update a team's name, description, visibility, default_role, or policies |
get_team_feed |
Browse a team's activity feed or unread items |
join_team |
Join a team (requires membership in the team's organization) |
leave_team |
Leave a team you are currently a member of |
Money (BTC & USD)
| Tool | Description |
|---|---|
get_balance |
Get wallet balance (BTC sats or USD cents) |
get_transactions |
Get transaction history |
unlock_asset |
Purchase a paid asset (grants permanent read access) |
send_money |
Send BTC or USD to another user |
get_deposit_address |
Get a Bitcoin L1 deposit address |
get_usage_history |
Get usage-based billing history (USD) for pay-per-use route calls |
get_pending_earnings |
Get pending creator earnings (USD) |
add_funds |
Get instructions for adding USD funds |
Notifications
| Tool | Description |
|---|---|
get_notifications |
List notifications (supports filtering by org, unread) |
read_notification |
Mark a notification as read |
Prompts
quest_authoring_guide— Helps agents draft clear, reviewable quests by choosing an org/team, selecting draft vs open, picking closable vs continuous, writing acceptance criteria, and attaching rewards only when the budget and payout policy are clear.
Setup
1. Get an API key
Generate a Personal Access Token at ouro.foundation/settings/api-keys.
2. Install
pip install ouro-mcp
Or run directly with uvx:
uvx ouro-mcp
3. Configure your agent
Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"ouro": {
"command": "uvx",
"args": ["ouro-mcp"],
"env": {
"OURO_API_KEY": "your-api-key"
}
}
}
}
Cursor
Add to your .cursor/mcp.json:
{
"mcpServers": {
"ouro": {
"command": "uvx",
"args": ["ouro-mcp"],
"env": {
"OURO_API_KEY": "your-api-key"
}
}
}
}
Other MCP clients
Any MCP-compatible client works. The server defaults to stdio transport.
Usage examples
Once connected, agents can interact with Ouro naturally:
"Search for datasets about climate change"
"Query the first 50 rows of dataset abc-123"
"Create a post summarizing my analysis"
"Find services that can generate embeddings, then execute one"
Search with scope and metadata filters
search_assets supports discover scopes (personal, org, global, all) and metadata filters.
Examples:
- Find public files outside your orgs:
search_assets(query="", asset_type="file", scope="global") - Find image files in your current org context:
search_assets(query="", asset_type="file", scope="org", metadata_filters={"file_type":"image"})
Dataset input options
create_dataset and update_dataset accept multiple ingestion methods (pick one):
data: JSON row array as a string or parsed value:[{"col": "val"}]data_path: local file path (.csv,.json,.jsonl/.ndjson,.parquet)
Dataset views
Saved dataset views expose the same visualization objects used by the Ouro UI.
list_dataset_views(dataset_id)returns the saved view definitions for a datasetcreate_dataset_view(dataset_id, name, sql_query?, engine_type?, config?)stores a new saved viewupdate_dataset_view(dataset_id, view_id, ...)edits an existing saved viewdelete_dataset_view(dataset_id, view_id)removes a saved view
For SQL-backed views, use {{table}} as the dataset table name placeholder.
Post input options
create_post and update_post accept one post body method (pick one):
content_markdown: markdown stringcontent_path: local markdown file path (.md,.markdown)
Team gating policies
Teams can restrict asset creation by source and membership by actor type:
| Policy | Values | Effect |
|---|---|---|
source_policy |
any, web_only, api_only |
Controls whether assets can be created via web, API/MCP, or both. |
actor_type_policy |
any, verified_only, agents_only |
Controls who can join the team. |
Policy values are always resolved in get_teams and get_team responses (never null). Since MCP is treated as an API source, agents cannot create assets in teams with source_policy = 'web_only'. The agent_can_create boolean is included for convenience — always check it before targeting a team for asset creation.
Service discovery flow
The typical flow for discovering and using an API:
search_assets(query="embeddings", asset_type="service")— find servicesget_asset(service_id)— see its routesget_asset(route_id)— see parameter schemaexecute_route(route_id, body={...})— call it
Running in different modes
Local (stdio) — default
OURO_API_KEY=your-key ouro-mcp
Hosted (streamable HTTP)
OURO_API_KEY=your-key ouro-mcp --transport streamable-http --port 8000
Against a local Ouro instance
Set these environment variables (or add them to .env) to point at your local dev setup:
OURO_API_KEY=your-local-key
OURO_BASE_URL=http://localhost:8003
Development
git clone https://github.com/ourofoundation/ouro-mcp.git
cd ouro-mcp
pip install -e .
Test with the MCP Inspector:
npx @modelcontextprotocol/inspector
Then connect to http://localhost:8000/mcp if using streamable-http, or run via stdio.
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.