MCP Chat Server
The MCP Chat Server provides document management capabilities, enabling programmatic interaction with stored text documents:
Read Documents (
read_doc_contents): Retrieve the full text contents of a document by providing its document ID (doc_id).Edit Documents (
edit_documents): Modify a document's content by specifying the document ID (doc_id), the exact text to replace (old_str), and the new text (new_str). The old string must match exactly, including whitespace.
The server also acts as a backend for AI chat applications, providing these document retrieval and editing services for AI models to utilize during interactive conversations.
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 Server summarize @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_documentsB
Edit a document by replacing a string in the documents content with a new string
Inputs: doc_id (required), old_str (required), new_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 have clearly distinct purposes: one reads document contents, the other edits them. There is no overlap or ambiguity.
Both tool names follow the verb_noun pattern (edit_documents, read_doc_contents), making them predictable and consistent.
With only 2 tools, the server feels under-scoped for a 'chat server' or even a document editor. Typically, such a server would need 3-15 tools to be useful.
For a document editing server, basic operations like create, delete, and list are missing. The server name implies chat functionality, which is completely absent.
Maintenance
Related MCP Connectors
Use your Mac, Windows or Linux computer from ChatGPT, Claude or Codex: files, commands, documents.
Persistent memory, hybrid search and a goal graph for AI agents, over stdio or remote HTTP.
Work on your own computers from Claude or ChatGPT: run commands, edit and search files.
Persistent context for Claude. Your AI always knows your projects and next actions across sessions.
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- FlicenseBqualityDmaintenanceEnables interactive chat with AI models and document retrieval via the Anthropic API, supporting command-based prompts and MCP tool integrations.2-
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- FlicenseCqualityDmaintenanceEnables interactive chat with AI models via Anthropic API, supporting document retrieval and command-based prompts through the MCP architecture.3-
- FlicenseNot gradedqualityCmaintenanceEnables interactive chat with AI models via the Anthropic API, supporting document retrieval, slash commands, and extensible MCP-based tool integrations.-