MCP Server (AI)

Alloy exposes an MCP (Model Context Protocol) server that lets you connect your AI coding and analysis tools directly to your mission data. Query missions, search instances, view reports, and more — right from your preferred AI tool.

Setting up

The MCP server URL:

https://aus.usealloy.ai/mcp

Connect it to your AI tool of choice using Streamable HTTP transport:

claude mcp add alloy --transport http https://aus.usealloy.ai/mcp

codex mcp add alloy --url https://aus.usealloy.ai/mcp

codex mcp login alloy

{
  "mcpServers": {
    "alloy": {
      "url": "https://aus.usealloy.ai/mcp"
    }
  }
}

{
  "mcpServers": {
    "alloy": {
      "serverUrl": "https://aus.usealloy.ai/mcp"
    }
  }
}

I need to connect to an MCP server. Here are the details:

- Server URL: https://aus.usealloy.ai/mcp
- Transport: Streamable HTTP (the URL is a single HTTP endpoint that accepts JSON-RPC POST requests)
- Authentication: OAuth 2.0 (browser-based consent flow — no API key or token needed upfront)
- Server name: "alloy"

Please configure this MCP server in my client. The server follows
the MCP Streamable HTTP transport specification. On first use it
will redirect me to a browser to authenticate with my Alloy
credentials via OAuth. No manual token setup is required.

What you can do

Read your data

• list_missions — search and list missions across your org
• get_mission_summary — full mission details including key events, metrics, and comments
• get_available_filters — discover what metadata filters exist for your missions
• get_plot_data — data points for a specific plot in a mission summary
• get_map_data — trajectory and geospatial data points
• search_instances — semantic image, log, or similarity search across your instance library
• get_instance — instance metadata, logs, images, timeseries, and detections
• list_scenarios / get_scenario — scenario list and match details
• list_reports / get_report / list_scheduled_reports — read reports and check schedules
• query_data — ask a natural language question about your data; the agent writes SQL and returns results
• search_docs — search Alloy product documentation

Create & edit (focused, single-shot)

Each of these runs a small focused agent — supply IDs you already know to keep them fast. All finish well inside any AI tool's request timeout. Edit tools are owner-only.

• create_report(prompt, mission_id?, mission_ids?) — compose a new shareable report from a brief
• update_report(report_id, prompt) — edit an existing report you own
• create_scheduled_report(prompt) — set up a recurring report (daily / weekly / fortnightly)
• update_scheduled_report(schedule_id, prompt) — edit a recurring schedule you own

Long-running tasks

For anything that needs multi-step exploration, deeper reasoning, or doesn't fit a single-shot tool — most notably creating or editing scenarios, where the scanner's detection strategy benefits from sample-mission validation — use the async pair:

• alloy_request_start(prompt, timeout_seconds?) — kick off a multi-step task, returns a task_id immediately. Defaults to a 25-minute server budget (matches the in-app agent's subagent cap); the agent self-terminates with whatever it has when the cap is hit.
• alloy_request_poll(task_id, wait_seconds?) — long-poll for completion (server blocks up to 60s); the response always carries progress so you can see what the agent is doing

Mesh Storage

• browse_mesh_storage — list files and folders in your mesh bucket (MCAP files come back with pipeline status baked in)
• get_mesh_file_download_url — presigned download URL for a specific file
• get_mesh_replay_url — dashboard URL that opens replay for one or more MCAPs (multi-file timeline). Files should overlap in time or be chronological for the best UX.
• get_mesh_inspect_url — dashboard URL that opens the Inspect MCAP modal for a single file (topics, diagnostics, ROS graph, capture config)
• query_mesh_storage — read-only DuckDB SQL against your Iceberg catalog
• list_mesh_tables — list tables or describe a specific table's schema
• get_mesh_connection_info — gateway URL and instructions for external clients (DuckDB, Spark, Trino). Alloy never issues mesh API keys over MCP — you generate those from the dashboard.

Device fleet & edge setup

• list_devices — every device with status, last-seen, last-upload, and file count
• approve_device / reject_device — lifecycle actions for pending devices
• get_edge_config / update_edge_config — read and write recorder config files
• get_docker_setup — full Docker bundle (AR token, login/pull commands, compose yaml). See Secret handling below.
• get_binary_setup — signed binary download URL plus edge-manager.yaml
• get_edge_manifest — available distros, tags, architectures, and binary releases

Secret handling

Device-setup tools return real short-lived AR access tokens (1-hour, read-only) so docker login commands are immediately usable. The provisioning key is never returned over MCP — compose and yaml templates come back with <YOUR_PROVISIONING_KEY> placeholders. Open Mesh Storage → Device Setup in the dashboard to download the same files with the real key embedded, or copy the provisioning key from that page into the template by hand.

Other actions stay UI-only for safety: deleting devices or files, rolling API keys, creating Mesh Storage API keys, and resetting edge configs.

Example usage

Once connected, you can ask your AI tool questions about your Alloy data:

• "What missions were uploaded this week?"
• "Summarize the latest mission report"
• "Find instances with navigation errors across all missions"
• "What scenarios are currently running?"
• "Compare controller error across the last 10 flights and write up the worst three" — uses alloy_request_start (analysis + selection upstream, then a report). The async pair will fetch the data and call create_report internally with concrete values.
• "Write a report titled 'Mission M Latency' for mission_id=m_abc with these metrics: p95=87ms, p99=142ms, errors=3" — uses create_report directly (concrete inputs, no analysis needed)
• "Schedule a weekly fleet-health summary for Monday 9am" — uses create_scheduled_report
• "Set up a scenario that flags any flight with a Z-axis position error over 0.5m" — uses alloy_request_start (scenarios need sample-mission validation, so they go through the async pair)

The AI tool uses Alloy's MCP server to fetch the data and respond in context — no need to switch to the Alloy web app.