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MCP Video Recognition Server

by mario-andreschak
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TypeScript
Remote

MCP Video Recognition Server

An MCP server that describes images, transcribes audio, and summarizes video from local files. It talks to Google Gemini by default, or to any OpenAI-compatible endpoint such as OpenRouter.

Features

  • Pick your provider: Google Gemini (default) or an OpenAI-compatible endpoint

  • Three MCP tools for local images, audio, and video

  • Optional Gemini model fallback, plus a final OpenAI-compatible backup

  • Opt-in multi-perspective parallel inference via PARALLEL_PROMPTS (see the Configuration Reference)

Model support varies by provider. Picking the OpenAI-compatible provider does not mean every endpoint or model handles every media type. And the server never swaps in a different model or provider on its own.

Related MCP server: Puter MCP Server

Prerequisites

  • Node.js 18.0.0 or later

  • An API key for your provider:

    • Gemini: GOOGLE_API_KEY

    • OpenAI-compatible: OPENAI_COMPATIBLE_API_KEY

Install

git clone https://github.com/yourusername/mcp-video-recognition.git
cd mcp-video-recognition
npm install
npm run build

Quickstart

Add the server to your MCP client config and point it at the built dist/index.js:

{
  "mcpServers": {
    "video-recognition": {
      "command": "node",
      "args": ["/path/to/mcp-video-recognition/dist/index.js"],
      "env": {
        "GOOGLE_API_KEY": "your_google_api_key"
      }
    }
  }
}

On Windows, use forward slashes or doubled backslashes (\\) in the path. Save the file and reconnect your MCP client. Environment variables must be present in the MCP client's env configuration; the server process does not automatically inherit values merely because a .env file exists beside it.

For OpenRouter or another OpenAI-compatible endpoint, set RECOGNITION_PROVIDER=openai-compatible and fill in its variables. Configuration has a ready-made example.

Standalone Client

The repository includes a client that builds the project, spawns the server over stdio, performs MCP initialization, and lists the tools. It loads .env from the repository root when that file exists; already-set process environment variables take precedence.

# Connect and list tools
npm run client

# Connect and call a tool
npm run client -- --tool image_recognition --args '{"filepath":"C:/media/example.png"}'

Use --help for server-path, working-directory, env-file, and timeout options:

npm run client -- --help

A tool error exits with status 2 and prints the complete MCP error result. Connection or configuration failures exit with status 1 and include the spawned server's stderr diagnostics.

With FLUJO:

  1. Click Add Server.

  2. Paste the GitHub URL.

  3. Click Parse, Clone, Install, Build and Save.

Configuration

The server reads environment variables. These are the ones you'll touch most:

Variable

Default

Purpose

RECOGNITION_PROVIDER

gemini

gemini or openai-compatible

GOOGLE_API_KEY

none

Gemini API key

GEMINI_MODEL

gemini-3.5-flash

Gemini model to use

OPENAI_COMPATIBLE_API_KEY

none

OpenAI-compatible API key

OPENAI_COMPATIBLE_BASE_URL

none

Endpoint base URL

OPENAI_COMPATIBLE_MODEL

none

Model to use

ALLOWED_MEDIA_ROOTS

none

Media directories for the OpenAI-compatible provider and Gemini backup

TRANSPORT_TYPE

stdio

stdio or streamable-http; sse is a legacy alias

MCP_HOST

127.0.0.1

Bind address for Streamable HTTP

PORT

3000

Bind port for Streamable HTTP

LOG_LEVEL

info

Diagnostic threshold; all logs go to stderr

A bad value stops startup. Nothing gets fixed silently.

The Configuration Reference has the full variable list, validation rules, an OpenRouter example, and the supported media types. For Gemini model fallback and the final backup, read the Provider Recovery Reference.

Tools

You get three MCP tools. Each takes a local filepath, an optional prompt (default Describe this content), and an optional modelname override.

  • image_recognition - describe an image

  • audio_recognition - transcribe or describe audio

  • video_recognition - describe a video

Example:

{
  "name": "video_recognition",
  "arguments": {
    "filepath": "/path/to/video.mp4",
    "prompt": "Describe what happens in this video"
  }
}

Security

  • HTTPS is required by default. Plain HTTP only works for a local endpoint you explicitly enable.

  • The OpenAI-compatible provider and the Gemini backup read media only from directories listed in ALLOWED_MEDIA_ROOTS. Containment is recursive, so specifying a parent folder (e.g. C:\Projects or ${workspaceFolder}) covers all repositories, subfolders, and media files inside it.

  • Keys stay in the process environment. Don't commit real keys.

The Security Reference covers endpoint rules, resource limits, and incident response.

MCP Inspector and Streamable HTTP

For stdio Inspector use, build first and make sure the API key is in Inspector's server environment. In PowerShell:

$env:GOOGLE_API_KEY = "your_google_api_key"
npm run debug

For HTTP, start the server separately:

$env:GOOGLE_API_KEY = "your_google_api_key"
$env:TRANSPORT_TYPE = "streamable-http"
npm start

Then select Streamable HTTP in Inspector and connect to http://127.0.0.1:3000/mcp. Do not select legacy SSE: the backwards-compatible TRANSPORT_TYPE=sse spelling still starts a Streamable HTTP endpoint.

Listing tools only proves the MCP handshake succeeded. It does not contact the recognition provider or read a media file. A later call can still fail because the key, model, network, or filepath is invalid. The path must identify a file visible to the spawned server process; paths from a different container or host will not work.

Development

# Build, connect through the standalone client, and list tools
GOOGLE_API_KEY=your_api_key npm run client

# Build and run the provider foundation tests
npm run verify:provider-foundation

Project Structure

  • src/index.ts: entry point and provider construction

  • src/server.ts: MCP server and transport

  • src/tools/: the three recognition tools

  • src/services/: Gemini and OpenAI-compatible providers

  • src/types/: shared types

  • src/utils/: helpers

License

MIT

Available Tools

3 tools
audio_recognitionB

Analyze and transcribe audio using Google Gemini AI

ParametersJSON Schema
NameRequiredDescriptionDefault
filepathYesPath to the media file to analyze
modelnameNoGemini model to use for recognitiongemini-2.0-flash
promptNoCustom prompt for the recognitionDescribe this content

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the full burden of behavioral disclosure. It only states 'analyze and transcribe' but does not detail output format, processing behavior, authentication needs, or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence, front-loaded with the core purpose. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description should explain what the tool returns (e.g., transcribed text or analysis). It does not, nor does it cover edge cases or prerequisites.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all parameters. The description does not add additional meaning beyond what the schema provides, meeting the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: analyze and transcribe audio using Google Gemini AI. It explicitly mentions 'audio' which distinguishes it from sibling tools image_recognition and video_recognition.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool vs alternatives (e.g., image_recognition, video_recognition). The description only states what it does without context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

image_recognitionB

Analyze and describe images using Google Gemini AI

ParametersJSON Schema
NameRequiredDescriptionDefault
filepathYesPath to the media file to analyze
modelnameNoGemini model to use for recognitiongemini-2.0-flash
promptNoCustom prompt for the recognitionDescribe this content

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It only mentions 'using Google Gemini AI' but does not disclose safety (e.g., read-only vs destructive), API costs, file size limits, or the nature of the analysis (e.g., real-time, batch).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with verb and resource. No wasted words. Efficient and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema is provided, yet the description does not explain what the tool returns (e.g., text description, confidence scores). For a tool with 3 parameters and no annotations, this leaves the agent guessing about the response format and behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with each parameter described. The description adds no additional meaning beyond the schema; it only names the AI provider. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Analyze and describe images') and the technology ('using Google Gemini AI'). It distinguishes from sibling tools (audio_recognition, video_recognition) by specifying the media type (images).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (e.g., audio_recognition, video_recognition). No mention of prerequisites, limitations, or scenarios where it is not appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

video_recognitionC

Analyze and describe videos using Google Gemini AI

ParametersJSON Schema
NameRequiredDescriptionDefault
filepathYesPath to the media file to analyze
modelnameNoGemini model to use for recognitiongemini-2.0-flash
promptNoCustom prompt for the recognitionDescribe this content

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool analyzes and describes videos but doesn't mention critical behavioral aspects like rate limits, authentication requirements, file size limits, supported video formats, processing time, or error handling. The description is too vague about what 'analyze and describe' entails operationally.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized and front-loaded with the essential information, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of video analysis (which typically involves format handling, processing time, and potential errors), no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how to interpret results, or any operational constraints, leaving significant gaps for an AI agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how the prompt interacts with video analysis or model selection trade-offs. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as analyzing and describing videos using Google Gemini AI, which is specific (verb+resource) and distinguishes it from sibling tools like audio_recognition and image_recognition. However, it doesn't explicitly mention video-specific capabilities beyond the name, leaving some ambiguity about whether it handles all video formats or specific features.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings (audio_recognition, image_recognition). It doesn't mention prerequisites, limitations, or alternative scenarios, leaving the agent to infer usage based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv1.0.0
    • First observedaudio_recognition
    • First observedimage_recognition
    • First observedvideo_recognition
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose targeting different media types: audio, images, and videos. There is no overlap in functionality, as they handle separate input formats with similar analysis capabilities but different domains.

Naming Consistency5/5

All tool names follow a consistent pattern of 'media_type_recognition' using snake_case. This predictable naming scheme makes it easy to understand what each tool does based on its name alone.

Tool Count3/5

With only 3 tools, the server feels somewhat thin for a video recognition domain, as it lacks operations like video editing, frame extraction, or metadata retrieval. However, the core recognition functions for audio, images, and videos are covered, making it borderline appropriate.

Completeness3/5

The server provides basic recognition for three media types but lacks comprehensive coverage for video processing. There are no tools for operations like video segmentation, object tracking, or format conversion, which are common in video recognition workflows, leaving notable gaps.

Maintenance

ActivityMaintained
ResponsivenessResponsive

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