Multimodal video search MCP server: connect an agent to your footage
A multimodal video search MCP server lets an AI assistant call media-search tools instead of asking a person to copy requests between applications. The V-Modal server exposes collection discovery and finding tools over supported transports. This guide uses release v0.2.0_20261022_161211, commit 8a7003fe64ea400c482863ef7b5dfbe24c057930, as its source for names and configuration.
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What the multimodal video search MCP server connects
MCP provides a tool boundary between an assistant and a service. The assistant can discover available operations, provide structured arguments and read the returned result. In this workflow, V-Modal performs media retrieval and the assistant helps choose a collection, phrase a query and explain the candidates. The tool result still needs to preserve source identity and time context for human inspection.
Treat the assistant as the caller of a defined contract, not as an index or a media player. It cannot infer that a local recording has been uploaded and indexed simply because a filename appears in chat. Begin with an available collection and a known searchable item, then check each tool’s output before chaining it into a longer agent workflow.
Install the pinned vmodal_mcp source
The README describes installation of the Python package from Git. Pin the reviewed commit for a reproducible setup instead of using a moving branch. Install it into the Python environment you already use for this integration, then verify that the vmodal-mcp command is available to the assistant process. A command that works in an interactive terminal may not be on a desktop client’s launch path.
Keep the package source and the assistant connection configuration as separate checks. Installation makes the executable available; the client still needs to launch it with the desired transport and runtime credentials. Use the release README’s instructions for your assistant. This page shows setup declarations only; it does not claim a live authenticated search was run on your collection.
python -m pip install "git+https://github.com/v-modal/vmodal_mcp.git@8a7003fe64ea400c482863ef7b5dfbe24c057930"Reference: 8a7003fe64ea400c482863ef7b5dfbe24c057930 README.md Git installation, pinned instead of main
Configure stdio for an assistant client
The tagged README’s MCP configuration launches vmodal-mcp with run and --transport stdio. It passes the runtime credential through api_key. Replace the placeholder in your own assistant configuration and keep it out of a public example. The command and argument array are separate fields; do not combine them into an invented executable name or assume a shell will parse them for the client.
Stdio describes the connection between the assistant and this local server process. It does not mean that video retrieval happens entirely on the local machine. The server calls the applicable V-Modal services for the active identity. If the assistant cannot discover tools, first inspect process startup and the configured command path before diagnosing collection access or search relevance.
{
"mcpServers": {
"vmodal": {
"command": "vmodal-mcp",
"args": ["run", "--transport", "stdio"],
"env": { "api_key": "PUT_API_KEY_HERE" }
}
}
}Reference: 8a7003fe64ea400c482863ef7b5dfbe24c057930 README.md stdio client configuration
Check health, identity and collection discovery
The pinned registry includes health, auth_me and collection_list. Health checks gateway or API availability; auth_me returns the current profile; collection_list discovers collections with their modes and available indexes. Those are distinct questions. A healthy gateway does not establish that the configured identity can search the collection named in a user’s request.
Ask the assistant to list accessible collections before selecting one. Carry the returned collection name into subsequent calls rather than generating a likely name. The registry describes omitting mode to list every mode. Keep the result available to the agent so it can explain an unavailable collection without fabricating a successful search or guessing which account owns the requested media.
Use find for a concrete agent search workflow
The find tool accepts collection_name and query_text, with optional sub-collection and other documented inputs. The registry describes automatic mode and version resolution when omitted, so the assistant should not guess an index version. A straightforward workflow is to identify the active account, list collections, choose the intended collection and ask for a visible scene in that footage.
Have the assistant return the source context with each candidate and distinguish a retrieved match from a verified observation. The registry also documents optional saved frame images and server-local image paths. A path on the server is not automatically a file on the user’s phone or another client. State where any saved image lives before asking someone to open it.
Evaluate agent behavior when the answer is uncertain
Try a missing collection, an empty result and a query unrelated to the test footage. The assistant should report those states from tool output rather than filling in a story about what happened in the recording. For an inspection task, request the original timestamp and surrounding playback so a person can confirm the scene before treating the answer as evidence.
Keep startup, authentication, collection discovery, retrieval and image saving as separate checkpoints in a pilot. Record the pinned commit and the failing tool when reporting a problem. This makes an agent workflow reproducible even if the assistant’s wording changes between runs, while keeping the underlying collection and result contract visible to the team reviewing the integration.