See how to build an MCP server with FastMCP that makes network automation conversational using Claude Desktop.
🎥 Watch: cs.co/90007ikmg
#NetworkAutomation #AI #CiscoRADKit #MCP #DevNet
See how to build an MCP server with FastMCP that makes network automation conversational using Claude Desktop.
🎥 Watch: cs.co/90007ikmg
#NetworkAutomation #AI #CiscoRADKit #MCP #DevNet
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AIに外部APIを使わせたい?MCPサーバーを30分で実装する | Zennの「AI」のフィード
AIと外部サービスを安全に連携させる標準プロトコル、MCP(Model Context Protocol)と、その専用APIサーバーであるMCPサーバーについて解説する。
MCPはAIが外部ツールやデータを利用するための共通規格となる。
この記事は、PythonのFastMCPフレームワークを使ったMCPサーバーの具体的な構築手順を、外部API連携の例を交えながらハンズオン形式で紹介し、AIエージェントへの接続方法まで網羅する。
AIに外部APIを使わせたい?MCPサーバーを30分で実装する | Zennの「AI」のフィード
AIと外部サービスを安全に連携させる標準プロトコル、MCP(Model Context Protocol)と、その専用APIサーバーであるMCPサーバーについて解説する。
MCPはAIが外部ツールやデータを利用するための共通規格となる。
この記事は、PythonのFastMCPフレームワークを使ったMCPサーバーの具体的な構築手順を、外部API連携の例を交えながらハンズオン形式で紹介し、AIエージェントへの接続方法まで網羅する。
#monitoring #devops #observability https://opsmtrs.com/3iSdf3y
#monitoring #devops #observability https://opsmtrs.com/3iSdf3y
It’s a practical look at how to instrument MCP tool calls with middleware: spans, arguments, errors, and metadata.
If you’re running your own MCP server, it’s worth a read.
www.scoutapm.com/blog/adding-...
It’s a practical look at how to instrument MCP tool calls with middleware: spans, arguments, errors, and metadata.
If you’re running your own MCP server, it’s worth a read.
www.scoutapm.com/blog/adding-...
High-performance C++ implementation of Model Context Protocol (MCP) with support for device, resource, signaling, and multiple transport layers (STDIO, HTTP/SSE, WebSocket). fastmcpp is a C++ port of the Python fastmcp library, providing native…
High-performance C++ implementation of Model Context Protocol (MCP) with support for device, resource, signaling, and multiple transport layers (STDIO, HTTP/SSE, WebSocket). fastmcpp is a C++ port of the Python fastmcp library, providing native…
L: https://github.com/0xeb/fastmcpp
C: https://news.ycombinator.com/item?id=45924417
posted on 2025.11.14 at 01:25:51 (c=1, p=8)
L: https://github.com/0xeb/fastmcpp
C: https://news.ycombinator.com/item?id=45924417
posted on 2025.11.14 at 01:25:51 (c=1, p=8)
5leaf.jp/kindle/B0G2JKKQ8H/#a...
5leaf.jp/kindle/B0G2JKKQ8H/#a...
I'll show how to...
🛠️ Build MCP servers with FastMCP
🤖 Connect servers to agents
☁️ Deploy to the cloud
🔐 Add auth (API keys, OAuth2/PRM/DCR)
All free, on YouTube, with OSS code samples. Register:
🔗 […]
I'll show how to...
🛠️ Build MCP servers with FastMCP
🤖 Connect servers to agents
☁️ Deploy to the cloud
🔐 Add auth (API keys, OAuth2/PRM/DCR)
All free, on YouTube, with OSS code samples. Register:
🔗 […]
You'll learn how to...
🛠️ Build MCP servers with FastMCP
🤖 Connect servers to agents
☁️ Deploy to the cloud
🔐 Add auth (API keys, OAuth2/PRM/DCR)
All free, on YouTube, with OSS code samples. Register:
🔗 aka.ms/PythonMCP/se...
You'll learn how to...
🛠️ Build MCP servers with FastMCP
🤖 Connect servers to agents
☁️ Deploy to the cloud
🔐 Add auth (API keys, OAuth2/PRM/DCR)
All free, on YouTube, with OSS code samples. Register:
🔗 aka.ms/PythonMCP/se...
Pydantic-AI agent connected to a FastMCP server (deployed on FastMCP cloud) using different LLM models from the new Pydantic AI Gateway, called via a Vercel AI React frontend, with both agent and MCP server sending Otel logs to Logfire.
Pydantic-AI agent connected to a FastMCP server (deployed on FastMCP cloud) using different LLM models from the new Pydantic AI Gateway, called via a Vercel AI React frontend, with both agent and MCP server sending Otel logs to Logfire.
Pydantic-AI agent connected to a FastMCP server (deployed on FastMCP cloud) using different LLM models from the new Pydantic AI Gateway, called via a Vercel AI React frontend, with both agent and MCP server sending Otel logs to Logfire.
Pydantic-AI agent connected to a FastMCP server (deployed on FastMCP cloud) using different LLM models from the new Pydantic AI Gateway, called via a Vercel AI React frontend, with both agent and MCP server sending Otel logs to Logfire.
Why? Agents are bad at polling- they over/under-check.
SEP-1686 moves orchestration to MCP itself:
github.com/modelcontext...
For Python devs, MCPClient in FastMCP will implement.
Why? Agents are bad at polling- they over/under-check.
SEP-1686 moves orchestration to MCP itself:
github.com/modelcontext...
For Python devs, MCPClient in FastMCP will implement.
Hear from builders at Goose, LiquidMetal AI, FastMCP, Apollo & Google as they demo MCP-UI, Raindrop, Firestore integrations and more.
Connect with fellow MCP developers and see it in action.
🔗 RSVP: https://luma.pulse.ly/nn8ln6engr
Hear from builders at Goose, LiquidMetal AI, FastMCP, Apollo & Google as they demo MCP-UI, Raindrop, Firestore integrations and more.
Connect with fellow MCP developers and see it in action.
🔗 RSVP: https://luma.pulse.ly/nn8ln6engr
https://kripta.biz/posts/994D60BC-F19B-4B8E-878A-54DD560F9382
https://kripta.biz/posts/994D60BC-F19B-4B8E-878A-54DD560F9382
TWiT+ Club Shows: AI User Group #9 - DIY MCP Server
Building a Custom AI Task Manager in Minutes With FastMCP
with @antnielsen.bsky.social, Darren Oakey
This episode is exclusive to Club TWiT.
Join at https://twit.tv/clubtwit
TWiT+ Club Shows: AI User Group #9 - DIY MCP Server
Building a Custom AI Task Manager in Minutes With FastMCP
with @antnielsen.bsky.social, Darren Oakey
This episode is exclusive to Club TWiT.
Join at https://twit.tv/clubtwit
- LiteLLM (Trial): A necessary tool for integrating multiple providers.
- FastMCP (Trial): A good attempt to standardize server boilerplate.
These are fine. But they don't solve the real problem.
- LiteLLM (Trial): A necessary tool for integrating multiple providers.
- FastMCP (Trial): A good attempt to standardize server boilerplate.
These are fine. But they don't solve the real problem.