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MCP Chat

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Python
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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

  1. Create or edit the .env file in the project root and verify that the following variables are set correctly:

CLAUDE_MODEL="claude-sonnet-4-5"
ANTHROPIC_API_KEY=""  # Enter your Anthropic API secret key

# Set to 1 if you're using uv to run the project
# Set to 0 if you're *not* using uv
USE_UV=1

Step 2: Install dependencies

Option 1: Setup with uv (Recommended)

uv is a fast Python package installer and resolver.

  1. Install uv, if not already installed:

pip install uv

  1. Create and activate a virtual environment:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

  1. Install dependencies:

uv pip install -e .

  1. Run the project

uv run main.py

Option 2: Setup without uv

  1. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

  1. Install dependencies:

pip install anthropic python-dotenv prompt-toolkit "mcp[cli]==1.8.0"

  1. Run the project

python main.py

Usage

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.md

Commands

Use the / prefix to execute commands defined in the MCP server:

> /summarize deposition.md

Commands 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:

  1. Complete the TODOs in mcp_server.py

  2. Implement the missing functionality in mcp_client.py

Linting and Typing Check

There are no lint or type checks implemented.

Available Tools

2 tools
edit_documentA

Edit a document by replacing a string in the documents content with a new string

ParametersJSON Schema
NameRequiredDescriptionDefault
doc_idYesId of the document that will be edited
new_strYesThe new text to insert in place of old text
old_strYesThe text to replace. Must match exactly, including whitespaces.

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the operation and does not mention side effects (e.g., persistence, overwriting), failure conditions (e.g., old_str not found), or whether the edit applies to all occurrences. This is a minimal disclosure for a mutating operation.

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, efficient sentence with no fluff. The core action and resource are front-loaded, and every word contributes to understanding the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is relatively simple (3 required string params, no output schema). The description covers the essential operation, but it omits behavioral details like what happens if old_str is not found or if multiple matches exist. For a mutating tool without annotations, this is a minor but noticeable gap.

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%, so all parameters have descriptions in the schema. The description itself adds no new semantic detail beyond what the schema already provides; it just reiterates the replace action. This meets the baseline for high coverage.

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 clearly states the action (edit), the resource (a document), and the specific operation (replacing a string with a new string). This is a specific verb+resource that distinguishes it from the sibling tool read_doc_contents, which is focused on reading.

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?

The description implies usage (when you need to modify content) but provides no explicit guidance on when to use this tool versus the sibling read_doc_contents, nor does it mention any prerequisites or exclusions. The context is clear but lacks differentiation.

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
NameRequiredDescriptionDefault
doc_idYesId of the document to read

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It does state the output behavior ('return it as a string'), but does not mention error handling, permissions, or what happens when the document does not exist. For a simple read operation, this is adequate but not rich.

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 with no wasted words. It efficiently conveys both the operation and the return format.

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?

This is a simple tool with one parameter, full schema coverage, and no output schema. The description is largely complete for a read operation, especially since it explicitly states the return type. It could be slightly stronger with error or permission context, but nothing essential is missing for basic invocation.

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?

The schema already fully documents the only parameter, doc_id, as 'Id of the document to read', so schema coverage is 100%. The description adds no additional parameter meaning beyond the schema, matching the baseline score.

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 states a specific verb and resource: 'Read the contents of a document' and explicitly notes the return type as a string. This clearly distinguishes it from the sibling 'edit_document', which implies modification rather than reading.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The verb 'Read' establishes a clear context for when this tool is appropriate, and its contrast with 'edit_document' implies read-only usage. However, it does not explicitly state when not to use it or name the alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv0.1.0
    • First observededit_document
    • First observedread_doc_contents
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 2 tools

Disambiguation5/5

The two tools are completely distinct: one reads document contents, the other edits them. There is no overlap or ambiguity in their purposes.

Naming Consistency4/5

Both names follow a verb_noun pattern, but one uses the abbreviation 'doc' while the other uses 'document', and one refers to 'contents' while the other uses 'document'. Minor inconsistency, but the pattern is still readable and predictable.

Tool Count3/5

With only two tools, the server feels thin for a document-related service. While each tool has a clear purpose, the low count is borderline for being considered a well-scoped set.

Completeness2/5

The server supports reading and editing but lacks create, delete, or listing capabilities. Editing is limited to string replacement, which restricts the possible workflows agents can perform.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Related MCP Connectors

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