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OmniMCP

Semantic router for MCP ecosystems

Discover and execute tools across multiple MCP servers without context bloat

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The Problem

MCP tool definitions consume tokens fast. A typical setup:

Server	Tools	Tokens
GitHub	35	~26K
Slack	11	~21K
Filesystem	8	~5K
Database	12	~8K

That's 60K+ tokens before the conversation starts. Add more servers and you're competing with your actual context.

Why this matters

Context bloat kills performance. When tool definitions consume 50-100K tokens, you're left with limited space for actual conversation, documents, and reasoning. The model spends attention on tool schemas instead of your task.

More tools = more hallucinations. With 50+ similar tools (like notification-send-user vs notification-send-channel), models pick wrong tools and hallucinate parameters. Tool selection accuracy drops as the toolset grows.

Dynamic tool loading breaks caching. Loading tools on-demand during inference seems smart, but it invalidates prompt caches. Every new tool added mid-conversation means reprocessing the entire context. Your "optimization" becomes a performance tax.

Tool results bloat context too. A single file read can return 50K tokens. An image is 1K+ tokens base64-encoded. These pile up in conversation history, pushing out earlier context.

The Solution

OmniMCP exposes a single semantic_router tool as the only entry point to your entire MCP ecosystem. The LLM never sees individual tool definitions—just one unified interface.

Traditional: 58 tools → 55K tokens in tool definitions
OmniMCP:      1 tool   → ~500 tokens (semantic_router)

How it works:

1. Semantic search → Find relevant tools by intent, not name. Tools are pre-indexed with embeddings, searched at runtime, returned as text results (not tool definitions)
2. Lazy server loading → Servers start only when needed, shutdown when done
3. Progressive schema loading → Full JSON schema fetched only before execution, returned as text in conversation
4. Content offloading → Large results chunked, images described, stored for retrieval

Key insight: Tool schemas appear in conversation text, not in tool definitions. This means:

• Prompt caching stays intact (tool definition never changes)
• Only relevant schemas enter context (via search results)
• No hallucination from similar tool names (model sees 3-5 tools, not 50+)

From ~60K tokens to ~3K. Access to everything, cost of almost nothing.

Architecture: Meta-Tool Pattern

OmniMCP uses the meta-tool pattern, similar to Claude Code's Agent Skills system. Instead of exposing dozens of individual tools, it exposes a single semantic_router meta-tool that acts as a gateway to your entire MCP ecosystem.

Progressive Disclosure Workflow

Architecture & Request Flow

How It Works

Traditional MCP approach:

tools: [
  {name: "github_create_issue", description: "..."},
  {name: "github_create_pr", description: "..."},
  {name: "filesystem_read", description: "..."},
  // ... 50+ more tools
]

❌ Problems: Context bloat, tool hallucination, no caching

OmniMCP's meta-tool approach:

{
  "tools": [
    {
      "name": "semantic_router",
      "description": "Universal gateway to MCP ecosystem...\n
        OPERATIONS: search_tools, get_server_info, execute_tool...\n
        LIST OF INDEXED SERVERS:\n
        filesystem: 8 tools (file operations, read/write)\n
        github: 35 tools (issues, PRs, repos)\n
        ...",
      "input_schema": {
        "operation": "search_tools | execute_tool | ..."
      }
    }
  ]
}

✅ Benefits: Single tool definition, server list in description, dynamic discovery

Parallel to Claude Skills

Claude Skills and OmniMCP share the same architectural insight:

Aspect	Claude Skills	OmniMCP
Meta-tool	Skill tool	semantic_router tool
Discovery	Skill descriptions in tool description	Server list + hints in tool description
Invocation	Skill(command="skill-name")	semantic_router(operation="execute_tool", server_name=...)
Context injection	Skill instructions loaded on invocation	Tool schemas fetched on-demand, returned as text
Cache-friendly	Tool definition never changes	Tool definition never changes
Dynamic list	<available_skills> section	LIST OF INDEXED SERVERS section
Behavioral hints	Skill descriptions guide LLM	Server hints field guides LLM

Key insight: Both systems inject instructions through prompt expansion rather than traditional function calling. The tool description becomes a dynamic directory that the LLM reads and reasons about, while actual execution details are loaded lazily.

Example of behavioral guidance:

Claude Skills:

<available_skills>
  skill-creator: "When user wants to create a new skill..."
  internal-comms: "When user wants to write internal communications..."
</available_skills>

OmniMCP hints:

{
  "elevenlabs": {
    "hints": [
      "When the user requests audio generation (speech or music), always execute in background mode",
      "Proactively offer to play audio using the ElevenLabs tool when contextually relevant"
    ]
  }
}

Both inject behavioral instructions that shape how the LLM uses the tools, not just what they do.

Real-World Example

Multi-server orchestration in action:

Claude Code generated a video using OmniMCP(previously pulsar-mcp) to coordinate multiple MCP servers.

The process:

1. Exa (background mode) - Parallel web searches to gather latest news
2. Modal Sandbox - Computed and generated video from news data
3. Filesystem - Retrieved the video file from Modal's output
4. ffmpeg - Transformed video to GIF format

The workflow:

search_tools("web search news")
  → execute_tool(exa, search, in_background=True) × 3 parallel calls
  → poll_task_result() → aggregate results
  → execute_tool(modal-sandbox, generate_video, data)
  → execute_tool(filesystem, read_file, video_path)
  → execute_tool(modal-sandbox, ffmpeg_convert, video_to_gif)

What this demonstrates:

• Semantic discovery - Finding the right tools across servers without knowing their exact names
• Background execution - Parallel Exa searches without blocking the conversation
• Cross-server coordination - Modal → Filesystem → Modal pipeline with automatic state management
• Single interface - All operations through one semantic_router tool

Without OmniMCP: 50+ tool definitions, complex orchestration, context overflow. With OmniMCP: Discover → Execute → Coordinate. Seamlessly.

Installation

uv pip install omnimcp // or uv add omnimcp 

Configuration

Environment Variables

OmniMCP requires several environment variables to operate. You must configure these before running index or serve commands.

Required variables:

Variable	Description	Example
OPENAI_API_KEY	OpenAI API key for embeddings and descriptions	sk-proj-...
QDRANT_DATA_PATH	Path to local directory for embedded Qdrant vector database (no separate server needed)	/path/to/qdrant_data
TOOL_OFFLOADED_DATA_PATH	Path for storing offloaded content (large results, images)	/path/to/tool_offloaded_data

Optional variables (with defaults):

Variable	Default	Description
EMBEDDING_MODEL_NAME	text-embedding-3-small	OpenAI embedding model
DESCRIPTOR_MODEL_NAME	gpt-4.1-mini	Model for generating tool descriptions
VISION_MODEL_NAME	gpt-4.1-mini	Model for describing images
MAX_RESULT_TOKENS	5000	Chunk threshold for large results
DESCRIBE_IMAGES	true	Use vision to describe images
DIMENSIONS	1024	Embedding dimensions

Setup methods:

Option 1: Export directly

export OPENAI_API_KEY="sk-proj-..."
export QDRANT_DATA_PATH="/path/to/qdrant_data"
export TOOL_OFFLOADED_DATA_PATH="/path/to/tool_offloaded_data"

Option 2: Create a .env file

# .env
OPENAI_API_KEY=sk-proj-...
QDRANT_DATA_PATH=/path/to/qdrant_data
TOOL_OFFLOADED_DATA_PATH=/path/to/tool_offloaded_data

Then source it before running commands:

source .env  # or use: export $(cat .env | xargs)
uvx omnimcp index --config-path mcp-servers.json

Note: For stdio transport, environment variables must also be included in your MCP client config (see stdio transport section below).

Quick Start

1. Create your MCP servers config (mcp-servers.json):

This is an enhanced schema of Claude Desktop's MCP configuration with additional OmniMCP-specific fields.

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",  // or "uvx", "docker", any executable
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/directory"],
      "env": {},  // optional: environment variables for this server
      "timeout": 30.0,  // optional: seconds to wait for MCP server startup (default: 30)
      "hints": ["file operations", "read write files"],  // optional: help semantic search discover this server
      "blocked_tools": [],  // optional: tools to index but block at runtime
      "ignore": false,  // optional: skip indexing this server entirely
      "overwrite": false  // optional: force re-indexing even if already indexed
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {"GITHUB_TOKEN": "..."},
      "blocked_tools": ["delete_repository", "fork_repository"]  // indexed but execution blocked
    },
    "elevenlabs": {
      "command": "uvx",
      "args": ["elevenlabs-mcp"],
      "env": {"ELEVENLABS_API_KEY": "..."},
      "hints": [
        "When the user requests audio generation (speech or music), always execute in background mode",
        "Proactively offer to play audio using the ElevenLabs tool when contextually relevant"
      ]
    },
    "exa": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.exa.ai/mcp"],
      "env": {"EXA_API_KEY": "..."},
      "hints": [
        "Always execute web searches in background mode to avoid blocking the conversation",
        "Multiple Exa tool calls can be fired in parallel to efficiently gather information from different sources"
      ]
    }
  }
}

Enhanced Configuration Fields:

Field	Type	Description
command	string	Executable to run (npx, uvx, docker, python, etc.)
args	array	Command-line arguments passed to the executable
env	object	Environment variables for this MCP server
timeout	number	Seconds to wait for server startup (default: 30.0)
hints	array	Powerful! Guide the LLM on how to use this server. Used for both discovery and execution instructions. Examples: "Always execute in background mode", "Multiple calls can be fired in parallel", "Proactively offer when contextually relevant"
blocked_tools	array	Tool names that will be indexed but blocked from execution
ignore	boolean	If true, skip indexing this server entirely (default: false)
overwrite	boolean	If true, force re-index even if already indexed (default: false)

Note: The command, args, and env fields are standard MCP configuration. The other fields are OmniMCP enhancements for better control and discovery.

2. Set environment variables:

export OPENAI_API_KEY="sk-..."
export QDRANT_DATA_PATH="/path/to/qdrant_data"
export TOOL_OFFLOADED_DATA_PATH="/path/to/tool_offloaded_data"

3. Index your servers (recommended before serving):

uvx omnimcp index --config-path mcp-servers.json

4. Run the server:

# Default: HTTP transport (recommended)
uvx omnimcp serve --config-path mcp-servers.json --transport http --host 0.0.0.0 --port 8000

# stdio transport (for local MCP clients - requires pre-indexing)
uvx omnimcp serve --config-path mcp-servers.json --transport stdio

Transport Modes

OmniMCP supports two transport protocols, each optimized for different deployment scenarios:

HTTP Transport (Default - Recommended)

Best for most use cases: remote access, web integrations, or when using with mcp-remote or mcp-proxy.

uvx omnimcp serve --config-path mcp-servers.json --transport http --host 0.0.0.0 --port 8000

Use cases:

• Remote access - Serve OmniMCP on a server, connect from anywhere
• Multiple clients - Share one OmniMCP instance across multiple agents
• Web integrations - REST API access to your MCP ecosystem
• mcp-remote/mcp-proxy - Expose OmniMCP through MCP proxy layers

Example with mcp-remote:

npm install mcp-remote

{
  "mcpServers": {
    "omnimcp-remote": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://your-server:port/mcp"]
    }
  }
}

HTTP mode advantages:

• Indexing happens once on server startup
• Multiple clients share the same indexed data

stdio Transport

Best for local MCP clients that communicate via standard input/output (Claude Desktop, Cline, etc.).

⚠️ IMPORTANT: You MUST run uvx omnimcp index before using stdio mode to avoid slow startup times.

uvx omnimcp serve --config-path mcp-servers.json --transport stdio

Recommended workflow:

1. Index first - Run omnimcp index before adding to your MCP client config
2. Then mount - Add OmniMCP to your client's MCP configuration
3. Start serving - Client launches OmniMCP automatically via stdio

Example client config (claude_desktop_config.json):

{
  "mcpServers": {
    "omnimcp": {
      "command": "uvx",
      "args": ["omnimcp", "serve"],
      "env": {
        "CONFIG_PATH": "/path/mcp/config/file",
        "TRANSPORT": "stdio|http",
        "OPENAI_API_KEY": "sk-...",
        "QDRANT_DATA_PATH": "/path/to/qdrant_data",
        "TOOL_OFFLOADED_DATA_PATH": "/path/to/tool_offloaded_data"
        // add other env keys
      }
    }
  }
}

Why index before mounting? Indexing can take time with many servers. Pre-indexing ensures instant startup when your MCP client launches OmniMCP.

Troubleshooting uvx Issues

Error: spawn uvx ENOENT or command not found: uvx

This means uv is not installed or not in your PATH. Detailed troubleshooting guide • Official MCP docs.

Quick fixes:

1. Install uv (if not installed):

   # macOS/Linux
   brew install uv
   
   # or official installer
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Ensure uv is in PATH (macOS/Linux):

   # Check if installed
   which uvx
   
   # If not found, add to PATH (add to ~/.zshrc or ~/.bashrc)
   export PATH="$HOME/.local/bin:$PATH"
   
   # Or create symlink
   sudo ln -s ~/.local/bin/uvx /usr/local/bin/uvx
   

3. Use absolute path in config (find with which uvx):

   "command": "/Users/you/.local/bin/uvx"  // macOS
   "command": "C:\\Users\\you\\.local\\bin\\uvx.exe"  // Windows
   

Alternative: Use mcp-remote for HTTP mode

If uvx issues persist, run OmniMCP via HTTP and connect through mcp-remote:

# Terminal 1: Run OmniMCP HTTP server
uvx omnimcp serve --config-path mcp-servers.json --transport http --port 8000

// Claude Desktop config
{
  "mcpServers": {
    "omnimcp": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:8000/mcp"]
    }
  }
}

This bypasses stdio issues and works reliably across platforms.

How It Works

OmniMCP exposes a single semantic_router tool that acts as a gateway to your entire MCP ecosystem:

search_tools("read CSV files and analyze data")
    → Returns: filesystem.read_file, database.query (ranked by relevance)

get_tool_details("filesystem", "read_file")
    → Returns: Full JSON schema with parameters

manage_server("filesystem", "start")
    → Launches the server process

execute_tool("filesystem", "read_file", {"path": "/data/sales.csv"})
    → Returns: File content (chunked if large)

Operations

Operation	Description
search_tools	Semantic search across all indexed tools
get_server_info	View server capabilities and limitations
list_server_tools	Browse tools on a specific server
get_tool_details	Get full schema before execution
manage_server	Start or shutdown server instances
list_running_servers	Show active servers
execute_tool	Run tools with optional background mode
poll_task_result	Check background task status
get_content	Retrieve offloaded content by reference

Content Management

Large tool results are automatically handled:

• Text > 5000 tokens → Chunked, preview returned with reference ID
• Images → Offloaded, described with GPT-4 vision, reference returned
• Audio → Offloaded, reference returned

Retrieve full content with get_content(ref_id, chunk_index).

Background Execution

For long-running tools:

# Queue for background execution
execute_tool("server", "slow_tool", args, in_background=True, priority=1)
# Returns: task_id

# Check status
poll_task_result(task_id)
# Returns: status, result when done

Development

git clone https://github.com/milkymap/omnimcp.git
cd omnimcp
uv sync
uv run pytest

Related Research

OmniMCP builds on emerging research in scalable tool selection for LLM agents:

ScaleMCP: Dynamic and Auto-Synchronizing Model Context Protocol Tools

Lumer et al. (2025) introduce ScaleMCP, addressing similar challenges in MCP tool selection at scale. Their approach emphasizes:

• Dynamic tool retrieval - Giving agents autonomy to discover and add tools during multi-turn interactions
• Auto-synchronizing storage - Using MCP servers as the single source of truth via CRUD operations
• Tool Document Weighted Average (TDWA) - Novel embedding strategy that selectively emphasizes critical tool document components

Their evaluation across 5,000 financial metric servers demonstrates substantial improvements in tool retrieval and agent invocation performance, validating the importance of semantic search in MCP ecosystems.

Key insight: Both OmniMCP and ScaleMCP recognize that traditional monolithic tool repositories don't scale. The future requires dynamic, semantic-first approaches to tool discovery.

Anthropic's Advanced Tool Use

Anthropic's Tool Search feature (2025) introduces three capabilities that align with OmniMCP's architecture:

• Tool Search Tool - Discover thousands of tools without consuming context window
• Programmatic Tool Calling - Invoke tools in code execution environments to reduce context impact
• Tool Use Examples - Learn correct usage patterns beyond JSON schema definitions

Quote from Anthropic: "Tool results and definitions can sometimes consume 50,000+ tokens before an agent reads a request. Agents should discover and load tools on-demand, keeping only what's relevant for the current task."

This mirrors OmniMCP's core philosophy: expose minimal interface upfront (single semantic_router tool), discover tools semantically, load schemas progressively.

Convergent Evolution

These independent efforts converge on similar principles:

1. Semantic discovery over exhaustive enumeration
2. Progressive loading over upfront tool definitions
3. Agent autonomy to query and re-query tool repositories
4. Context efficiency as a first-class design constraint

OmniMCP implements these principles through semantic routing, lazy server loading, and content offloading—making large-scale MCP ecosystems practical today.

License

MIT License - see LICENSE for details.

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Built after several months of research and development

Multiple architectural iterations • Real-world agent deployments • Extensive testing across diverse MCP ecosystems

OmniMCP emerged from solving actual problems in production agent systems where traditional approaches failed. Every feature—from semantic routing to background execution to content offloading—was battle-tested against the challenges of scaling MCP ecosystems beyond toy examples.

We hope OmniMCP will be useful to the community in building more capable and efficient agent systems.

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