OmniMCP

Semantic router for MCP ecosystems

Discover and execute tools across multiple MCP servers without context bloat

The Problem

MCP tool definitions consume tokens fast. A typical setup:

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

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:

❌ Problems: Context bloat, tool hallucination, no caching

OmniMCP's meta-tool approach:

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

Parallel to Claude Skills

Claude Skills and OmniMCP share the same architectural insight:

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:

OmniMCP hints:

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:

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

Configuration

Environment Variables

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

Required variables:

Optional variables (with defaults):

Setup methods:

Option 1: Export directly

Option 2: Create a

Then source it before running commands:

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.

Enhanced Configuration Fields:

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:

3. Index your servers (recommended before serving):

4. Run the server:

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.

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 :

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

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):

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:

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:

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:

Operations

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:

Development

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.

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.