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docs: Update FastMCP import paths and dependencies
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@@ -76,7 +76,7 @@ Connections between clients and servers are established through transports like
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The fastest way to build MCP servers is with FastMCP, which provides a high-level, Pythonic interface:
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The fastest way to build MCP servers is with FastMCP, which provides a high-level, Pythonic interface:
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```python
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```python
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from fastmcp import FastMCP
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from mcp.server.fastmcp import FastMCP
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mcp = FastMCP("Demo")
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mcp = FastMCP("Demo")
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@@ -93,7 +93,7 @@ def get_greeting(name: str) -> str:
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FastMCP handles all the complex protocol details and server management, so you can focus on building great tools. It's designed to be high-level and Pythonic - in most cases, decorating a function is all you need.
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FastMCP handles all the complex protocol details and server management, so you can focus on building great tools. It's designed to be high-level and Pythonic - in most cases, decorating a function is all you need.
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For more information about FastMCP, see the [FastMCP documentation](https://github.com/jlowin/fastmcp).
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FastMCP was originally developed by Jeremiah Lowin at [jlowin/fastmcp](https://github.com/jlowin/fastmcp). We are grateful for his contribution in developing this excellent framework that has now been integrated into MCP.
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### Low-Level Implementation
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### Low-Level Implementation
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@@ -1,8 +1,8 @@
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# /// script
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# /// script
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# dependencies = ["pydantic-ai-slim[openai]", "asyncpg", "numpy", "pgvector", "fastmcp"]
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# dependencies = ["pydantic-ai-slim[openai]", "asyncpg", "numpy", "pgvector"]
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# ///
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# ///
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# uv pip install 'pydantic-ai-slim[openai]' asyncpg numpy pgvector fastmcp
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# uv pip install 'pydantic-ai-slim[openai]' asyncpg numpy pgvector
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"""
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"""
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Recursive memory system inspired by the human brain's clustering of memories.
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Recursive memory system inspired by the human brain's clustering of memories.
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@@ -1,5 +1,5 @@
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# /// script
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# /// script
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# dependencies = ["fastmcp"]
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# dependencies = []
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# ///
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# ///
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"""
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"""
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