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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:

ANTHROPIC_API_KEY=""  # Enter your Anthropic API secret key

Step 2: Install dependencies

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

3 tools
add_intsC

Add to integers together

ParametersJSON Schema
NameRequiredDescriptionDefault
aYesFirst number to add
bYesSecond number to add

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.2/5.0
Behavior1/5

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 transparency. It only says 'add' without disclosing return format, error handling, type enforcement, or side effects. Additionally, it describes inputs as 'integers' while the schema types them as strings, creating a confusing behavioral implication.

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?

The description is extremely brief and front-loaded, but it contains a grammatical error ('to integers' instead of 'two integers') and lacks any structural organization. It is not a well-formed sentence, and the brevity works against clarity.

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?

The tool is simple and an output schema exists, but the description fails to explain return values, edge cases, or how this tool fits into a larger workflow. The integer/string contradiction also makes it incomplete and potentially misleading for a seemingly trivial operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides 100% parameter description coverage ('First number to add', 'Second number to add'), so the baseline would be 3. However, the description adds conflicting information by calling the inputs 'integers' when the schema declares them as strings, which could mislead the agent into passing the wrong type or expecting integer-specific behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: adding integers together. However, the phrasing 'Add to integers together' is grammatically awkward, and it does not explicitly distinguish from sibling tools, though the siblings are clearly unrelated (reading/editing documents). Overall, the purpose is understandable.

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

Usage Guidelines1/5

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

The description provides no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. There is no mention of context where this tool is appropriate or inappropriate, leaving the agent without any usage heuristics.

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

edit_doc_contentsB

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

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

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It does not mention destructive nature, behavior when old_str is not found, case sensitivity, or side effects. The exact-match requirement appears only in the schema, not the description.

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 sentence with the verb front-loaded and no wasted words. It is concise and easily scanned.

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?

The tool has no output schema and no annotations, so the description should explain return values and edge cases. It omits what happens on success/failure, whether it replaces all occurrences, and any prerequisites, making it incomplete for a mutation tool.

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%, so baseline is 3. The description does not add extra meaning beyond what the schema already provides for doc_id, old_str, and new_str.

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 tool edits a document by replacing a string, using the specific verb 'Edit' with the resource 'document' and the operation 'replacing a string'. This distinguishes it from sibling tools like read_doc_contents (read) and add_ints (arithmetic).

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 vs. alternatives, no prerequisites, and no exclusions. The description merely states the action without context on appropriate usage scenarios.

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

A3.8/5.0
Behavior3/5

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

No annotations are present, so the description carries the burden. It communicates the core read-only action and return format, but omits details about error handling, permissions, or potential side effects. The term 'read' implies safety, but this is not explicitly stated.

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 states the action and return type. No superfluous words or redundant information, making it highly concise.

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 tool with one parameter and no output schema, the description covers the essentials: what it acts on and what it returns. It lacks error handling details, but given the low complexity, it is sufficiently complete for basic usage.

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 input schema already provides a complete description of the doc_id parameter (100% coverage). The tool description adds no additional parameter semantics, so the baseline score of 3 is appropriate.

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 ('read'), the resource ('document'), and the return type ('string'). It distinguishes itself from siblings like edit_doc_contents and add_ints by focusing on retrieval, making the purpose unambiguous.

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 for reading documents but does not explicitly compare with edit_doc_contents or specify when to choose this tool over alternatives. The context is clear from the verb, but no direct guidance is given.

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. Dates show when Glama detected each change.

  1. 3 tool updatesv0.1.0
    • First observedadd_ints
    • First observededit_doc_contents
    • First observedread_doc_contents

TDQS

C2.9/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: integer addition, document reading, and document editing. Even the two document tools are unambiguous because one reads content and the other modifies it.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: add_ints, read_doc_contents, edit_doc_contents. This creates a predictable and readable naming convention.

Tool Count2/5

The server is named 'MCP Chat' but contains only three tools unrelated to chat (math and document editing). The count is small and the set feels arbitrary and under-scoped for the stated purpose.

Completeness1/5

The tool set is severely incomplete for a chat server—no messaging or chat-related operations exist. Even within the included domains, there are omissions: document tools lack create/delete, and the math tool only supports addition.

Maintenance

ActivitySlowing
ResponsivenessSyncing

Resources

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