LLM Bridge MCP는 AI 에이전트가 표준화된 인터페이스를 통해 여러 대규모 언어 모델과 상호 작용할 수 있도록 지원합니다. 메시지 제어 프로토콜(MCP)을 활용하여 다양한 LLM 제공업체에 원활하게 접근할 수 있도록 하여 모델 간 전환을 용이하게 하고 동일한 애플리케이션에서 여러 모델을 사용할 수 있도록 지원합니다.
여러 LLM 공급자에 대한 통합 인터페이스:
OpenAI(GPT 모델)
인류학적(클로드 모델)
구글(제미니 모델)
딥시크
...
유형 안전 및 검증을 위해 Pydantic AI로 구축됨
온도 및 최대 토큰과 같은 사용자 정의 가능한 매개변수를 지원합니다.
사용 추적 및 측정 항목을 제공합니다.
Related MCP server: MindBridge MCP Server
서버는 다음 도구를 구현합니다.
지엑스피1
prompt : LLM에 보낼 텍스트 프롬프트
model_name : 사용할 특정 모델(기본값: "openai:gpt-4o-mini")
temperature : 무작위성을 제어합니다(0.0~1.0)
max_tokens : 생성할 최대 토큰 수
system_prompt : 모델의 동작을 안내하는 선택적 시스템 프롬프트
Smithery를 통해 Claude Desktop에 llm-bridge-mcp를 자동으로 설치하려면:
npx -y @smithery/cli install @sjquant/llm-bridge-mcp --client claude저장소를 복제합니다.
git clone https://github.com/yourusername/llm-bridge-mcp.git
cd llm-bridge-mcpuv를 설치하세요(아직 설치되지 않았다면):
# On macOS
brew install uv
# On Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# On Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"API 키로 루트 디렉토리에 .env 파일을 만듭니다.
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_API_KEY=your_google_api_key
DEEPSEEK_API_KEY=your_deepseek_api_keyClaude Desktop 구성 파일이나 .cursor/mcp.json 에 서버 항목을 추가합니다.
"mcpServers": {
"llm-bridge": {
"command": "uvx",
"args": [
"llm-bridge-mcp"
],
"env": {
"OPENAI_API_KEY": "your_openai_api_key",
"ANTHROPIC_API_KEY": "your_anthropic_api_key",
"GOOGLE_API_KEY": "your_google_api_key",
"DEEPSEEK_API_KEY": "your_deepseek_api_key"
}
}
}이 오류는 시스템이 PATH에서 uvx 실행 파일을 찾을 수 없을 때 발생합니다. 해결 방법은 다음과 같습니다.
해결 방법: uvx의 전체 경로를 사용하세요
uvx 실행 파일의 전체 경로를 찾으세요.
# On macOS/Linux
which uvx
# On Windows
where.exe uvx그런 다음 MCP 서버 구성을 업데이트하여 전체 경로를 사용하세요.
"mcpServers": {
"llm-bridge": {
"command": "/full/path/to/uvx", // Replace with your actual path
"args": [
"llm-bridge-mcp"
],
"env": {
// ... your environment variables
}
}
}이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.
Run a prompt through an LLM and return the response.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| model_name | No | Specific model name. Available models: anthropic:claude-3-7-sonnet-latest, anthropic:claude-3-5-haiku-latest, anthropic:claude-3-5-sonnet-latest, anthropic:claude-3-opus-latest, claude-3-7-sonnet-latest, claude-3-5-haiku-latest, bedrock:amazon.titan-tg1-large, bedrock:amazon.titan-text-lite-v1, bedrock:amazon.titan-text-express-v1, bedrock:us.amazon.nova-pro-v1:0, bedrock:us.amazon.nova-lite-v1:0, bedrock:us.amazon.nova-micro-v1:0, bedrock:anthropic.claude-3-5-sonnet-20241022-v2:0, bedrock:us.anthropic.claude-3-5-sonnet-20241022-v2:0, bedrock:anthropic.claude-3-5-haiku-20241022-v1:0, bedrock:us.anthropic.claude-3-5-haiku-20241022-v1:0, bedrock:anthropic.claude-instant-v1, bedrock:anthropic.claude-v2:1, bedrock:anthropic.claude-v2, bedrock:anthropic.claude-3-sonnet-20240229-v1:0, bedrock:us.anthropic.claude-3-sonnet-20240229-v1:0, bedrock:anthropic.claude-3-haiku-20240307-v1:0, bedrock:us.anthropic.claude-3-haiku-20240307-v1:0, bedrock:anthropic.claude-3-opus-20240229-v1:0, bedrock:us.anthropic.claude-3-opus-20240229-v1:0, bedrock:anthropic.claude-3-5-sonnet-20240620-v1:0, bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0, bedrock:anthropic.claude-3-7-sonnet-20250219-v1:0, bedrock:us.anthropic.claude-3-7-sonnet-20250219-v1:0, bedrock:cohere.command-text-v14, bedrock:cohere.command-r-v1:0, bedrock:cohere.command-r-plus-v1:0, bedrock:cohere.command-light-text-v14, bedrock:meta.llama3-8b-instruct-v1:0, bedrock:meta.llama3-70b-instruct-v1:0, bedrock:meta.llama3-1-8b-instruct-v1:0, bedrock:us.meta.llama3-1-8b-instruct-v1:0, bedrock:meta.llama3-1-70b-instruct-v1:0, bedrock:us.meta.llama3-1-70b-instruct-v1:0, bedrock:meta.llama3-1-405b-instruct-v1:0, bedrock:us.meta.llama3-2-11b-instruct-v1:0, bedrock:us.meta.llama3-2-90b-instruct-v1:0, bedrock:us.meta.llama3-2-1b-instruct-v1:0, bedrock:us.meta.llama3-2-3b-instruct-v1:0, bedrock:us.meta.llama3-3-70b-instruct-v1:0, bedrock:mistral.mistral-7b-instruct-v0:2, bedrock:mistral.mixtral-8x7b-instruct-v0:1, bedrock:mistral.mistral-large-2402-v1:0, bedrock:mistral.mistral-large-2407-v1:0, claude-3-5-sonnet-latest, claude-3-opus-latest, cohere:c4ai-aya-expanse-32b, cohere:c4ai-aya-expanse-8b, cohere:command, cohere:command-light, cohere:command-light-nightly, cohere:command-nightly, cohere:command-r, cohere:command-r-03-2024, cohere:command-r-08-2024, cohere:command-r-plus, cohere:command-r-plus-04-2024, cohere:command-r-plus-08-2024, cohere:command-r7b-12-2024, deepseek:deepseek-chat, deepseek:deepseek-reasoner, google-gla:gemini-1.0-pro, google-gla:gemini-1.5-flash, google-gla:gemini-1.5-flash-8b, google-gla:gemini-1.5-pro, google-gla:gemini-2.0-flash-exp, google-gla:gemini-2.0-flash-thinking-exp-01-21, google-gla:gemini-exp-1206, google-gla:gemini-2.0-flash, google-gla:gemini-2.0-flash-lite-preview-02-05, google-gla:gemini-2.0-pro-exp-02-05, google-vertex:gemini-1.0-pro, google-vertex:gemini-1.5-flash, google-vertex:gemini-1.5-flash-8b, google-vertex:gemini-1.5-pro, google-vertex:gemini-2.0-flash-exp, google-vertex:gemini-2.0-flash-thinking-exp-01-21, google-vertex:gemini-exp-1206, google-vertex:gemini-2.0-flash, google-vertex:gemini-2.0-flash-lite-preview-02-05, google-vertex:gemini-2.0-pro-exp-02-05, gpt-3.5-turbo, gpt-3.5-turbo-0125, gpt-3.5-turbo-0301, gpt-3.5-turbo-0613, gpt-3.5-turbo-1106, gpt-3.5-turbo-16k, gpt-3.5-turbo-16k-0613, gpt-4, gpt-4-0125-preview, gpt-4-0314, gpt-4-0613, gpt-4-1106-preview, gpt-4-32k, gpt-4-32k-0314, gpt-4-32k-0613, gpt-4-turbo, gpt-4-turbo-2024-04-09, gpt-4-turbo-preview, gpt-4-vision-preview, gpt-4.5-preview, gpt-4.5-preview-2025-02-27, gpt-4o, gpt-4o-2024-05-13, gpt-4o-2024-08-06, gpt-4o-2024-11-20, gpt-4o-audio-preview, gpt-4o-audio-preview-2024-10-01, gpt-4o-audio-preview-2024-12-17, gpt-4o-mini, gpt-4o-mini-2024-07-18, gpt-4o-mini-audio-preview, gpt-4o-mini-audio-preview-2024-12-17, groq:gemma2-9b-it, groq:llama-3.1-8b-instant, groq:llama-3.2-11b-vision-preview, groq:llama-3.2-1b-preview, groq:llama-3.2-3b-preview, groq:llama-3.2-90b-vision-preview, groq:llama-3.3-70b-specdec, groq:llama-3.3-70b-versatile, groq:llama3-70b-8192, groq:llama3-8b-8192, groq:mixtral-8x7b-32768, mistral:codestral-latest, mistral:mistral-large-latest, mistral:mistral-moderation-latest, mistral:mistral-small-latest, o1, o1-2024-12-17, o1-mini, o1-mini-2024-09-12, o1-preview, o1-preview-2024-09-12, o3-mini, o3-mini-2025-01-31, openai:chatgpt-4o-latest, openai:gpt-3.5-turbo, openai:gpt-3.5-turbo-0125, openai:gpt-3.5-turbo-0301, openai:gpt-3.5-turbo-0613, openai:gpt-3.5-turbo-1106, openai:gpt-3.5-turbo-16k, openai:gpt-3.5-turbo-16k-0613, openai:gpt-4, openai:gpt-4-0125-preview, openai:gpt-4-0314, openai:gpt-4-0613, openai:gpt-4-1106-preview, openai:gpt-4-32k, openai:gpt-4-32k-0314, openai:gpt-4-32k-0613, openai:gpt-4-turbo, openai:gpt-4-turbo-2024-04-09, openai:gpt-4-turbo-preview, openai:gpt-4-vision-preview, openai:gpt-4.5-preview, openai:gpt-4.5-preview-2025-02-27, openai:gpt-4o, openai:gpt-4o-2024-05-13, openai:gpt-4o-2024-08-06, openai:gpt-4o-2024-11-20, openai:gpt-4o-audio-preview, openai:gpt-4o-audio-preview-2024-10-01, openai:gpt-4o-audio-preview-2024-12-17, openai:gpt-4o-mini, openai:gpt-4o-mini-2024-07-18, openai:gpt-4o-mini-audio-preview, openai:gpt-4o-mini-audio-preview-2024-12-17, openai:o1, openai:o1-2024-12-17, openai:o1-mini, openai:o1-mini-2024-09-12, openai:o1-preview, openai:o1-preview-2024-09-12, openai:o3-mini, openai:o3-mini-2025-01-31, test | openai:gpt-4o-mini |
| temperature | No | Controls randomness (0.0 to 1.0) | |
| max_tokens | No | Maximum number of tokens to generate | |
| system_prompt | No | Optional system prompt to guide the model's behavior |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral transparency. It fails to mention important traits like streaming, cost, latency, or error handling, leaving the agent uninformed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence, front-loading the action. It is not verbose, but it omits critical details, so it is not a perfect 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters, no output schema, and no annotations, the description is incomplete. It does not describe return values, optional parameters, or implications of choices like model or temperature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 80%, so the burden on the description is lower. However, the description adds no value beyond the schema—it does not explain any parameter semantics, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('run a prompt') and the resource ('LLM'), with the purpose of getting a response. It is specific and not a tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 versus alternatives. Since there are no sibling tools, the lack is less critical, but the description still offers no usage context or preconditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0run_llmScored across 1 tool
With only one tool, there is no possibility of ambiguity. The tool's purpose is clear and distinct.
A single tool means naming is trivially consistent. 'run_llm' is descriptive and follows a clear verb_noun pattern.
One tool for an LLM bridge is far too few. Typical servers in this domain include multiple tools for model selection, streaming, or context management.
The server offers only a basic 'run' operation with no supporting tools for model listing, configuration, or advanced features, leaving significant gaps.
One MCP endpoint for Claude, GPT & Gemini: 100+ tools + no-code connectors + agent workers.
The cross-LLM AI agent marketplace
Build, validate, and deploy multi-agent AI solutions from any AI environment.
run any ai model. compose agents, stack knowledge, connect tools. one api, pay per run.
