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Tools, helper endpoints, and playground

Inspect VideoVector MCP tool categories, use helper endpoints for config and tool discovery, and test tool execution through the MCP playground.

api/mcp_controllers.pymcp-server/src/tools/definitions.tsapi/routes.py

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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

MethodPathPurpose
GET/api/v2/mcp/statusInspect package name, install command, tool count, and supported platforms
GET/api/v2/mcp/configGenerate platform-specific config
GET/api/v2/mcp/toolsReturn tool definitions and category groupings
POST/api/v2/mcp/playgroundExecute 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, or multimodal_search
  • building prompts with create_prompt and test_prompt_schema
  • executing and monitoring runs with execute_prompt, get_prompt_run_status, and get_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_name
  • server_version
  • package_name
  • npm_install_command
  • documentation_url
  • tools_count
  • supported_platforms

Tool discovery response

GET /api/v2/mcp/tools returns:

  • total_count
  • tools
  • categories

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_name
  • success
  • result
  • error
  • execution_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.

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.

API reference

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.