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Search pages, API reference sections, and guide headings.
Summary
The public MCP helper endpoints expose tool definitions, category groupings, platform-specific configs, and direct playground execution so you can validate integrations before deploying them.
Helper endpoints
| Method | Path | Purpose |
|---|---|---|
GET | /api/v2/mcp/status | Inspect package name, install command, tool count, and supported platforms |
GET | /api/v2/mcp/config | Generate platform-specific config |
GET | /api/v2/mcp/tools | Return tool definitions and category groupings |
POST | /api/v2/mcp/playground | Execute a tool directly for testing |
Tool categories
The tool definition file groups public tools into categories such as:
- Search
- Discovery
- Video and segment inspection
- Prompt runs
- Prompt management
- Cloud connectors
- Import jobs
- Index management
- Video management
- Exports
- Webhooks
Representative workflows include:
- searching an index with
search_videos,search_videos_by_image, ormultimodal_search - building prompts with
create_promptandtest_prompt_schema - executing and monitoring runs with
execute_prompt,get_prompt_run_status, andget_prompt_run_results - operating imports, exports, connectors, and webhooks without leaving the MCP client
Status response
GET /api/v2/mcp/status returns fields such as:
server_nameserver_versionpackage_namenpm_install_commanddocumentation_urltools_countsupported_platforms
Tool discovery response
GET /api/v2/mcp/tools returns:
total_counttoolscategories
Each tool includes a public input schema that mirrors the MCP SDK Tool definition shape.
Playground execution
The playground endpoint accepts:
{
"tool_name": "search_videos",
"arguments": {
"index_id": "idx_archive",
"query": "reporter outside a station entrance",
"top_k": 5
}
}
The response includes:
tool_namesuccessresulterrorexecution_time_ms
The controller also enforces explicit scope rules. Read-only tools require read-level capability, and mutating tools require write.
Structured playground payloads
For several tool families, the controller wraps results in structuredContent plus text content so clients can render both machine-readable and human-readable output cleanly.
Use the playground to validate argument shapes and output structure before you hand the server to an agent or a production client.
Related documentation
The VideoVector MCP docs show how AI clients can browse indexes, run prompts, search media evidence, inspect workflow resources, and validate available tools.
The search API combines semantic retrieval, exact metadata filtering, cross-run search, playground search, and analyst-style SQL execution on run-backed tables.
Prompt runs are the execution boundary for extraction. The API exposes run creation, status inspection, cancellation, segment results, media-wide synthesis, failed-segment manifests, and debug-oriented LLM call access.
