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SDias14

MCP Chat

by SDias14

MCP Chat

MCP Chat is a command-line interface application that enables interactive chat capabilities with AI models through the Google Gemini API. The application supports document retrieval, command-based prompts, and extensible tool integrations via the MCP (Model Control Protocol) architecture.

Prerequisites

  • Python 3.9+

  • Google Gemini 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:

GEMINI_API_KEY=""  # Enter your Google Gemini API key
GEMINI_MODEL="gemini-3.6-flash"  # The Gemini model to use

You can generate an API key from Google AI Studio.

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 google-genai 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 the old text
old_strYesThe text to replace. Must match exactly, including whitespace

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 must fully disclose behavior. It states that a string is replaced, but does not clarify whether all occurrences are replaced or only the first, nor does it mention that the edit is permanent/mutating, any side effects, or error conditions. This ambiguity is a significant gap for a mutation tool.

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, compact sentence with no filler or redundant restatement of the tool name. It is front-loaded with the action and resource, and every word contributes meaning.

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?

For a simple 3-parameter tool, the description plus full schema coverage is mostly sufficient for an agent to make a call. However, the missing occurrence behavior (all vs. first match) and the absence of an output schema leave some uncertainty about the tool's actual effect and return value, making it minimally complete rather than fully contextualized.

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 covers 100% of the parameters with clear descriptions. The tool description essentially paraphrases the schema (old_str replaced by new_str) without adding new semantic meaning, such as constraints or examples. Baseline 3 is appropriate because the schema carries the burden.

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 uses a specific verb ('Edit'), names the exact resource ('a document'), and specifies the mechanism ('replacing a string ... with a new string'). This clearly distinguishes it from the sibling read_doc_contents, which is a read operation.

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 a document's content must be modified via string replacement. However, it provides no explicit guidance about when NOT to use it or when to prefer the sibling read_doc_contents instead. The alternative is only inferable from the tool name, not the description.

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. Dates show when Glama detected each change.

  1. 2 tool updatesv0.1.0
    • First observededit_document
    • First observedread_doc_contents

TDQS

B3.4/5.0

Scored across 2 tools

Disambiguation5/5

The two tools are clearly distinct: one reads document contents, the other edits by replacing a string. No ambiguity exists between them.

Naming Consistency4/5

Both use a verb_noun pattern (read_doc_contents, edit_document), but the noun part is slightly inconsistent ('doc_contents' vs 'document'). Minor deviation from perfect consistency.

Tool Count3/5

With only 2 tools, the server feels thin for a document-focused interface. It is borderline on the lower end of acceptable, as a minimal read/edit pair can work but lacks breadth.

Completeness2/5

The surface is severely limited—no create, delete, list, or search capabilities. For a document management domain, this leaves major gaps that would require agents to use other tools or fail.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • F
    license
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    quality
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    A command-line interface application for interactive chat with AI models via the Anthropic API. It supports document retrieval, command-based prompts, and extensible tool integrations through the MCP architecture.
    -
  • F
    license
    Not graded
    quality
    D
    maintenance
    A command-line interface application enabling interactive chat with AI models via the Anthropic API. It supports document retrieval, command-based prompts, and extensible tool integrations through the MCP architecture.
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