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MCP Chat

MCP Chat es una aplicación de interfaz de línea de comandos que permite capacidades de chat interactivo con modelos de IA a través de la API de Anthropic. La aplicación admite la recuperación de documentos, mensajes basados en comandos e integraciones de herramientas extensibles a través de la arquitectura MCP (Model Control Protocol).

Requisitos previos

  • Python 3.9+

  • Clave de API de Anthropic

Related MCP server: MCP Chat

Configuración

Paso 1: Configurar las variables de entorno

  1. Cree o edite el archivo .env en la raíz del proyecto y verifique que las siguientes variables estén configuradas correctamente:

ANTHROPIC_API_KEY=""  # Enter your Anthropic API secret key

Paso 2: Instalar dependencias

Opción 1: Configuración con uv (Recomendado)

uv es un instalador y resolvedor de paquetes de Python rápido.

  1. Instale uv, si aún no está instalado:

pip install uv
  1. Cree y active un entorno virtual:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. Instale las dependencias:

uv pip install -e .
  1. Ejecute el proyecto

uv run main.py

Opción 2: Configuración sin uv

  1. Cree y active un entorno virtual:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. Instale las dependencias:

pip install anthropic python-dotenv prompt-toolkit "mcp[cli]==1.8.0"
  1. Ejecute el proyecto

python main.py

Uso

Interacción básica

Simplemente escriba su mensaje y presione Enter para chatear con el modelo.

Recuperación de documentos

Utilice el símbolo @ seguido de un ID de documento para incluir el contenido del documento en su consulta:

> Tell me about @deposition.md

Comandos

Utilice el prefijo / para ejecutar comandos definidos en el servidor MCP:

> /summarize deposition.md

Los comandos se autocompletarán cuando presione Tab.

Desarrollo

Agregar nuevos documentos

Edite el archivo mcp_server.py para agregar nuevos documentos al diccionario docs.

Implementación de funciones MCP

Para implementar completamente las funciones MCP:

  1. Complete los TODOs en mcp_server.py

  2. Implemente la funcionalidad faltante en mcp_client.py

Linting y verificación de tipos

No hay comprobaciones de lint o de tipo implementadas.

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.

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

TDQS

A3.6/5.0

Scored across 2 tools

Disambiguation5/5

The two tools are clearly distinct: one reads document contents, the other edits by string replacement. No ambiguity in purpose.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern: read_doc_contents and edit_document. The naming style is uniform.

Tool Count2/5

With only two tools, the server feels extremely thin for any meaningful functionality, especially given the 'Chat' name suggests broader capabilities. The surface is too limited.

Completeness2/5

The toolset covers only read and edit operations, missing essential CRUD operations like create, delete, or list. This creates significant gaps for document management workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

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