LLM Gateway
Comprehensive MCP server exposing dozens of capabilities to AI agents: multi-provider LLM delegation, browser automation, document processing, vector ops, and cognitive memory systems
What is LLM Gateway?
LLM Gateway is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to comprehensive mcp server exposing dozens of capabilities to ai agents: multi-provider llm delegation, browser automation, document processing, vector ops, and cognitive memory systems
Comprehensive MCP server exposing dozens of capabilities to AI agents: multi-provider LLM delegation, browser automation, document processing, vector ops, and cognitive memory systems
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Comprehensive MCP server exposing dozens of capabilities to
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx llm-gateway-mcp-serverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use LLM Gateway
Ultimate MCP Server (LLM Gateway) is a comprehensive Python-based MCP server that exposes 40+ tools to AI agents, covering multi-provider LLM delegation (OpenAI, Anthropic, Google, DeepSeek, Grok, OpenRouter), browser automation via Playwright, document processing and OCR, SQL and vector database operations, a four-tier cognitive memory system, filesystem access, CLI tool wrapping, and audio transcription — all through a unified MCP interface with intelligent cost-optimized routing and semantic caching. It is designed to function as a complete AI agent operating system, potentially reducing API costs by 70-90% through intelligent task delegation and caching. Teams building complex multi-step AI agents use it as a single integration point that replaces dozens of individual MCP servers.
Prerequisites
- Python 3.13 or later and the uv package manager (https://docs.astral.sh/uv/)
- API keys for at least one LLM provider: OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, DEEPSEEK_API_KEY, OPENROUTER_API_KEY, or GROK_API_KEY
- Playwright browser binaries for browser automation tools (installed separately)
- Tesseract OCR engine if you plan to use PDF OCR capabilities
- An MCP-compatible client such as Claude Desktop, Cursor, or a custom agent
Install uv and clone the repository
uv is the required package manager. Install it then clone the repository.
curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/Dicklesworthstone/ultimate_mcp_server.git
cd ultimate_mcp_serverCreate a virtual environment and install dependencies
Use uv to create a Python 3.13 venv and install all optional extras.
uv venv --python 3.13
source .venv/bin/activate
uv sync --all-extrasCreate a .env file with your API keys
Configure at minimum one LLM provider key and the server's host/port. All provider keys are optional — only configure the ones you need.
# .env
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=AIza...
DEEPSEEK_API_KEY=...
GATEWAY_SERVER_PORT=8013
GATEWAY_SERVER_HOST=127.0.0.1
GATEWAY_CACHE_ENABLED=true
GATEWAY_CACHE_TTL=86400
LOG_LEVEL=INFOStart the MCP server
Launch the server using the umcp CLI. Use --include-tools or --exclude-tools to limit the active tool set for your use case.
umcp run
# Or with custom settings:
umcp run -h 0.0.0.0 -p 9000 -w 4
# Or exclude browser tools if Playwright is not installed:
umcp run --exclude-tools browser_init browser_navigateAdd the server to your MCP client configuration
Configure Claude Desktop or your agent to connect to the running Ultimate MCP Server via HTTP.
{
"mcpServers": {
"ultimate-mcp": {
"type": "http",
"url": "http://127.0.0.1:8013"
}
}
}(Optional) Install browser and OCR dependencies
Enable Playwright browser automation and Tesseract OCR support with these additional installation steps.
# Playwright browser
python -m playwright install chromium
# OCR support (requires Tesseract engine on host)
uv pip install -e ".[ocr]"
# macOS: brew install tesseract
# Ubuntu: apt-get install tesseract-ocrLLM Gateway Examples
Client configuration
Claude Desktop configuration connecting to Ultimate MCP Server running locally on the default port 8013.
{
"mcpServers": {
"ultimate-mcp": {
"type": "http",
"url": "http://127.0.0.1:8013"
}
}
}Prompts to try
Representative agent tasks that exercise the server's multi-provider LLM delegation, memory, browser automation, and document processing tools.
- "Use the cheapest available model to summarise this 50-page PDF and store the result in episodic memory"
- "Navigate to https://news.ycombinator.com, extract the top 10 story titles, and save them to a file"
- "Run this SQL query against the connected database and return the results as a CSV"
- "Benchmark gpt-4o vs claude-3-5-sonnet on this classification task using 5 iterations each"
- "Search your semantic memory for anything related to our previous discussion about vector databases"
- "Extract all tables from this Word document and convert them to structured JSON"Troubleshooting LLM Gateway
umcp command not found after installation
Ensure you have activated the virtual environment with: source .venv/bin/activate. The umcp CLI is installed inside the venv and is not available globally unless you install with uv tool install.
Browser tools fail with a Playwright not installed error
Run: python -m playwright install chromium inside the activated venv. Browser binaries are not installed by uv sync and must be downloaded separately. If you do not need browser tools, exclude them at startup: umcp run --exclude-tools browser_init browser_navigate
High API costs on first use
Enable semantic caching by setting GATEWAY_CACHE_ENABLED=true and GATEWAY_CACHE_TTL=86400 in your .env file. The cache deduplicates identical or semantically similar requests across sessions, which the project claims can reduce costs by 70-90% for repeated workloads.
Frequently Asked Questions about LLM Gateway
What is LLM Gateway?
LLM Gateway is a Model Context Protocol (MCP) server that comprehensive mcp server exposing dozens of capabilities to ai agents: multi-provider llm delegation, browser automation, document processing, vector ops, and cognitive memory systems It connects AI assistants to external tools and data sources through a standardized interface.
How do I install LLM Gateway?
Follow the installation instructions on the LLM Gateway GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with LLM Gateway?
LLM Gateway works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is LLM Gateway free to use?
Yes, LLM Gateway is open source and available under the NOASSERTION license. You can use it freely in both personal and commercial projects.
LLM Gateway Alternatives — Similar Coding Agents Servers
Looking for alternatives to LLM Gateway? Here are other popular coding agents servers you can use with Claude, Cursor, and VS Code.
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★ 24.5kA coding agent toolkit that provides IDE-like semantic code retrieval and editing tools, enabling LLMs to efficiently navigate and modify codebases using symbol-level operations instead of basic file reading and string replacements.
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