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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, prompts 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 características MCP

Para implementar completamente las características de 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_doc_contentC

Edits the content of a document based on the provided new content.

ParametersJSON Schema
NameRequiredDescriptionDefault
doc_idYesID of the document to edit.
old_strYesThe string to be replaced. Must match exactly, including whitespace.
new_strYesThe new string to replace the old string.

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations, the description must disclose important behaviors. It states 'Edits' implying mutation, but fails to mention that the operation is a find-and-replace, that it modifies data destructively, or any error conditions (e.g., if old_str is not found). The description is insufficient for safe invocation.

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 a single short sentence, which is concise but lacks structure. It repeats the tool name and does not prioritize key information. Every sentence should add value, and this one merely paraphrases the name without providing critical details.

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?

Given no output schema, no annotations, and three required parameters, the description should explain the return format, error behavior, and the find-and-replace process. It provides none of these, making the tool incomplete for an agent to use confidently.

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% with clear descriptions for all three parameters. The tool description adds no additional semantic value beyond the schema; it merely restates that editing occurs. Since the schema already covers full meaning, a score of 3 is appropriate.

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?

Description states 'Edits the content of a document', clearly indicating the verb and resource. It distinguishes from sibling 'read_doc_content' which is a read operation. However, it does not specify that the edit is a find-and-replace, which could lead to ambiguity about the exact editing mechanism.

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 is provided on when to use this tool versus its sibling or other alternatives. The description does not mention prerequisites, context, or limitations, leaving the agent to infer usage from the name and schema.

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

read_doc_contentA

Reads the content of a document and returns it as a string.

ParametersJSON Schema
NameRequiredDescriptionDefault
doc_idYesID 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 gives minimal behavioral context. It does not mention read-only status, access requirements, or any constraints like size limits.

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 no unnecessary words. It effectively communicates the tool's purpose.

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?

Given the tool's simplicity (1 parameter, no output schema) and the description's inclusion of return type as string, it is mostly complete. Could mention that the document must exist, but not critical.

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% with a clear parameter description. The description adds no extra meaning beyond the schema's doc_id field, so 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 it reads document content and returns a string, with a clear verb and resource. It distinguishes from the sibling tool edit_doc_content which modifies.

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 when-to-use or when-not-to-use guidance. The sibling tool edit_doc_content is listed, but the description does not provide usage context or alternatives.

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_doc_content
    • First observedread_doc_content

TDQS

B3.3/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one reads document content, the other edits it. There is no overlap or ambiguity.

Naming Consistency5/5

Both tool names follow a consistent verb_noun pattern (read_doc_content, edit_doc_content), making them predictable and easy to understand.

Tool Count4/5

With only two tools, the server is minimal but reasonably scoped for a chat-based document interaction context. It's slightly thin but not inappropriate.

Completeness2/5

The tools cover reading and editing existing documents, but lack essential operations like creating, deleting, or listing documents. This is a significant gap for a document-focused server.

Maintenance

ActivityInactive
ResponsivenessNo issues

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  • F
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    A
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    Enables interactive chat with AI models through the Anthropic API, supporting document retrieval, command-based prompts, and extensible tool integrations via the MCP architecture.
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