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modelcontextprotocol
python-sdk
Python2024–2026
modelcontextprotocol / python-sdk

python-sdk

The official Python SDK for Model Context Protocol servers and clients

Systems & InfrastructureAI & Machine LearningLibraries & SDKs
Explore on GitHubRead Hacker News discussions
8Distinct people
9Mentions
0Explicit recommenders
24.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The official Python SDK for Model Context Protocol servers and clients

GitHub

Where it is used

  • Home Assistant — The Model Context Protocol Client integration gives your AI conversation agent in Home Assistant extra tools from an MCP server of your choice. The integration calls mcp.client.streamable_http.streamable_http_client. Source

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month9 dated mentions

Observed mentions: 2024-11-25 17:58:50 UTC to 2026-03-25 17:19:06 UTC.

01232024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 commentsNov 20242024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJul 20252025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsMar 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC303
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC101
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC101
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC101
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC110
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC000
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC202

What Hacker News says

1 sources
Norcim133·Jul 21, 2025Submission

Show HN: Outlook MCP – I accidentally made the best email assistant

I couldn't understand the hype around MCP so I decided to build a simple server for Outlook (requires admin in Azure). For the build I just used a Python MCP SDK: https://github.com/modelcontextprotocol/python-sdk Now, I'm not a fast dev. Still, in 2 days I had Claude Desktop filing and responding to emails better than any assistant I've worked with (human or otherwise). None of this was due to my skill. I was basically just wrapping msgraph calls in the same @mcp.tool decorator over and over. In fact, I spent more time fiddling with msgraph auth & api-calls than I did on the MCP server itself. Today, in a typical workflow, I just tell Claude to "sort my inbox" and it handles everything from filing mails, creating folders, writing responses, scheduling meetings, flagging actions, etc. For anyone wondering about MCP, the following struck m
Read the rest
e: 1. It was the fastest "time-to-capable" I've had on any software build. 2. It was fully "human-capable" in a way that I have not experienced with "agentic workflow" approaches... it just worked. 3. There are nuances to how you "architect" your tool signatures for best results. For example, I had to name one parameter html_code_mail_body to prevent Claude from using plaintext in mails. 4. It was better to make pure, highly cohesive and atomic tools and let the LLM combine them for more complicated tasks. 5. It was so easy to build, all companies are likely to launch official MCP servers eventually. Overall, I think MCP offers a stark alternative to the "agentic workflows" model. In a technical sense, each step in a workflow is highly coupled to its upstream/downstream counterpart. This makes them both rigid and fragile. That's the source of some of the "RPA smells" people talk about with workflow agents. MCP is classic dependency inversion. The llm becomes an abstraction that can self-orchestrate using any tool it wants, in any order, based on the task set for it.
2 points · Open discussion

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