MCP DeepInfra AI Tools Server

Instructions

Implementation Reference

• src/mcp_deepinfra/server.py:85-96 (handler)

  The core handler function that executes the embeddings tool logic, generating embeddings for a list of input texts using the DeepInfra OpenAI-compatible API.
  async def embeddings(inputs: list[str]) -> str: """Generate embeddings for a list of texts using DeepInfra OpenAI-compatible API.""" model = DEFAULT_MODELS["embeddings"] try: response = await client.embeddings.create( model=model, input=inputs, ) embeddings_list = [item.embedding for item in response.data] return str(embeddings_list) except Exception as e: return f"Error generating embeddings: {type(e).__name__}: {str(e)}"
• src/mcp_deepinfra/server.py:83-84 (registration)

  Conditional registration of the embeddings tool using FastMCP's @app.tool() decorator based on ENABLED_TOOLS configuration.
  if "all" in ENABLED_TOOLS or "embeddings" in ENABLED_TOOLS: @app.tool()
• src/mcp_deepinfra/server.py:85-85 (schema)

  Function signature providing the input schema (inputs: list[str]) and output type (str) for automatic tool schema generation in FastMCP.
  async def embeddings(inputs: list[str]) -> str:
• src/mcp_deepinfra/server.py:34-34 (helper)

  Configuration for the default model used by the embeddings tool.
  "embeddings": os.getenv("MODEL_EMBEDDINGS", "sentence-transformers/all-MiniLM-L6-v2"),