F
licenseNot graded
qualityD
maintenanceA command-line interface application that enables interactive chat with AI models through the Anthropic API, supporting document retrieval, command-based prompts, and extensible tool integrations.
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 "Install 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 Serversummarize @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-doc-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
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| doc_id | Yes | Id of the document to edit | |
| old_str | Yes | The text to replace. Must match exactly, including whitespace | |
| new_str | Yes | The new text to insert in place of the old text |
TDQS
B3/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only says 'edit' implying mutation, but doesn't disclose whether changes are reversible, permission requirements, behavior if old_str not found, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short (one sentence), but lacks structure like explicit resource identification or behavioral notes. Conciseness is positive but insufficient content detracts.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, mutation tool with 3 required params. Missing critical details: return value, error handling, idempotency, or edge cases (e.g., multiple occurrences).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and descriptions are adequate. Description adds no extra meaning beyond schema (e.g., format, length limits, examples). Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Edit' and resource 'document', and replacing a string distinguishes it from sibling 'read_doc_contents' which only reads.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives or when not to use it. Sibling tool exists but no differentiation in usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_doc_contentsA
Read the contents of a document and return it as a string.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| doc_id | Yes | Id of the document to read |
TDQS
A3.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as side effects, permissions, rate limits, or safety guarantees. A simple read operation with minimal context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the action, resource, and output without any redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with one parameter and no output schema, the description is sufficiently complete. It could mention return format, but it's not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a single parameter 'doc_id' having a clear description. The tool description adds no additional semantic value beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action (read), resource (document), and output (string). It clearly distinguishes from the sibling tool 'edit_documents' which implies modification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit usage guidelines or when-not-to-use advice. The 'read' vs 'edit' distinction is implicit from the sibling tool name, but the description does not provide context for when to choose this tool over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
B3.3/5.0
Disambiguation5/5
The two tools have clearly distinct purposes: one reads document contents, the other edits them. There is no overlap or ambiguity.
Naming Consistency5/5
Both tool names follow the verb_noun pattern (edit_documents, read_doc_contents), making them predictable and consistent.
Tool Count2/5
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.
Completeness2/5
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
ActivityInactive
ResponsivenessNo issues
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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