I found this MCP tool recently: https://smithery.ai/server/@upstash/context7-mcp
Context 7, a software document retrieval tool and combined it with chain of thought reasoning using https://smithery.ai/server/@smithery-ai/server-sequential-thinking

Here's the prompt I used, it was rather helpful in improving accuracy and the overall experience:

You are a large language model equipped with a functional extension: Model Context Protocol (MCP) servers. You have been configured with access to the following tool:Context7 - a software documentation finder, combined with the SequentialThought chain-of-thought reasoning framework.

Tool Descriptions:

• resolve-library-idRequired first step: Resolves a general package name into a Context7-compatible library ID. This must be called before using get-library-docs to retrieve valid documentation.

• get-library-docsFetches up-to-date documentation for a library. You must first call resolve-library-id to obtain the exact Context7-compatible library ID.

• sequentialthinkingEnables chain-of-thought reasoning to analyze and respond to user queries.

Your task:

You will extensively use these tools when users ask questions about how a software package works. Your responses should follow this structured approach:

1. Analyze the user’s request to identify the type of query. Queries may be:

   • Creative: e.g., proposing an idea using a package and how it would work.

   • Technical: e.g., asking about a specific part of the documentation.

   • Error debugging: e.g., encountering an error and searching for a fix in the documentation.

2. Use SequentialThought to determine the query type.

3. For each query type, follow these steps:

   1. Generate your own idea or response based on the request.

   2. Find relevant documentation using Context7 to support your response and reference it.

   3. Reflect on the documentation and your response to ensure quality and correctness.

RESULTS:
I asked for a LangChain prompt chain system using MCP servers, and it gave me a very accurate response with examples straight from the docs!

TL;DR: Used Claude with local MCP tools to read and modify Word documents directly. It’s like having a coding assistant that can actually touch your files. What I did:

1.	Asked Claude to analyze a job requirements document - It used a 3-step semantic search process:
•	READ: Extracted all paragraphs from my .docx file
•	EMBED: Made the content searchable (though we hit some method issues here)
•	SEARCH: Found specific info about experience requirements
2.	Got detailed answers - Claude found that the job required:
•	17 years of IT experience overall
•	8 years in semantic technologies
•	8 years in technical standards (OWL, RDF, etc.)
•	Proven AI/ML experience
3.	Modified the document in real-time - Then I asked Claude to update specific paragraphs, and it actually changed the Word document on my machine:

•	Updated paragraph 14 to “Test MCP agent”
•	Updated paragraph 15 to “salut maman” (lol)

Why this is crazy: • Claude isn’t just reading or generating text anymore • It’s actually executing commands on my local system • Reading real files, modifying real documents • All through natural conversation The technical side: Claude used MCP commands like: • mcp.fs.read_docx_paragraphs to extract content • mcp.fs.update_docx_paragraphs to modify specific paragraphs

It even figured out the correct parameter formats through trial and error when I gave it the wrong method name initially. This feels like the future We’re moving from “AI that talks” to “AI that does”. Having an assistant that can read your documents, understand them, AND modify them based on conversation is wild. Anyone else experimenting with MCP? What local tools are you connecting to Claude?