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 Tell me about @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_starter_project
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
The two tools are clearly distinct: one reads document contents, the other modifies them. There is no overlap or ambiguity between the operations.
Both tools use a verb_noun pattern and snake_case, but one uses 'doc_contents' and the other uses 'document', which is a minor inconsistency. A more consistent pair would be read_document/edit_document.
Two tools is a thin surface and falls at the borderline for a useful server. The tools are focused, but the count feels minimal for a standalone document-related MCP server.
The tool surface covers reading and editing documents but lacks create, delete, list, and search capabilities. This leaves significant lifecycle gaps that agents may need to work around.
Maintenance
Related MCP Connectors
MCP server for AI dialogue using various LLM models via AceDataCloud
An MCP server that integrates with Discord to provide AI-powered features.
MCP server for OpenAI API (chat completions, image generation, embeddings) via AceDataCloud
Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.
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