MCP Chat
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Chat What are the key takeaways from deposition.md?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Chat
MCP Chat is a command-line interface application that enables interactive chat capabilities with AI models through the Anthropic API. The application supports document retrieval, command-based prompts, and extensible tool integrations via the MCP (Model Control Protocol) architecture.
Prerequisites
Python 3.9+
Anthropic API Key
Related MCP server: MCP Chat
Setup
Step 1: Configure the environment variables
Create or edit the
.envfile in the project root and verify that the following variables are set correctly:
ANTHROPIC_API_KEY="" # Enter your Anthropic API secret keyStep 2: Install dependencies
Option 1: Setup with uv (Recommended)
uv is a fast Python package installer and resolver.
Install uv, if not already installed:
pip install uvCreate and activate a virtual environment:
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activateInstall dependencies:
uv pip install -e .Run the project
uv run main.pyOption 2: Setup without uv
Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activateInstall dependencies:
pip install anthropic python-dotenv prompt-toolkit "mcp[cli]==1.8.0"Run the project
python main.pyUsage
Basic Interaction
Simply type your message and press Enter to chat with the model.
Document Retrieval
Use the @ symbol followed by a document ID to include document content in your query:
> Tell me about @deposition.mdCommands
Use the / prefix to execute commands defined in the MCP server:
> /summarize deposition.mdCommands will auto-complete when you press Tab.
Development
Adding New Documents
Edit the mcp_server.py file to add new documents to the docs dictionary.
Implementing MCP Features
To fully implement the MCP features:
Complete the TODOs in
mcp_server.pyImplement the missing functionality in
mcp_client.py
Linting and Typing Check
There are no lint or type checks implemented.
Available Tools
2 toolsedit_documentA
Edit a document by replacing a string in the documents content with a new string
Inputs: doc_id (required), new_str (required), old_str (required).
read_doc_contentsA
Read the contents of a document and return it as a string.
Inputs: doc_id (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
read_doc_contents and edit_document perform clearly distinct operations: reading versus modifying content. There is no functional overlap between the two tools.
Both names follow a verb_noun snake_case pattern, but 'doc' in read_doc_contents is abbreviated while 'document' is spelled out in edit_document. This is a minor inconsistency rather than a systemic problem.
Two tools is borderline: the set is small but each tool serves a clear purpose. It feels thin for a general document server, though it could be sufficient for a very narrow read/edit-only use case.
The domain appears to be document manipulation, but the surface is missing obvious operations like creating, deleting, listing, or searching documents. Edit is also limited to simple string replacement, which leaves significant workflow gaps.
Maintenance
Related MCP Connectors
Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
Talk to your public-facing AI from any MCP client — Claude, ChatGPT, Cursor, Cline, Windsurf.
Real-time chat for AI agents. Claude Code, Cursor, Cline and Codex join channels over MCP.
Private persistent memory for Claude, ChatGPT & Gemini via MCP - semantic search, zero-code setup.
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