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by hummbl-dev

mcp-agent-kit

Python Version Runtime Dependencies Protocol License

Un servidor y entorno de herramientas para el Model Context Protocol (MCP) basado exclusivamente en la biblioteca estándar, para Claude Desktop, Cursor y flotas de agentes autónomos.

Inicio rápido • Arquitectura • Configuración de Claude Desktop • Comparación • Rigor de ingeniería


Arquitectura


Related MCP server: MCP-123

¿Por qué mcp-agent-kit?

Los SDK oficiales de MCP incorporan decenas de dependencias pesadas de terceros (pydantic, anyio, httpx, starlette), lo que crea superficies de ataque innecesarias en la cadena de suministro y fricción de instalación en entornos empresariales.

mcp-agent-kit proporciona una implementación completa y lista para producción del Model Context Protocol utilizando 100% la biblioteca estándar de Python:

  • 🚀 Cero dependencias en tiempo de ejecución: Python puro (json, sys, inspect, dataclasses).

  • 🔒 Listo para entornos empresariales: auditabilidad inmediata sin exposición a vulnerabilidades de paquetes.

  • ⚡ Ultraligero: tiempo de arranque inferior a un milisegundo, ideal para herramientas CLI, entornos serverless e integraciones de agentes embebidos desplegados en el cliente.

  • 🛠️ Generación automática de esquemas: infiere definiciones de JSON Schema directamente de los type hints y docstrings nativos de Python.


mcp-agent-kit vs SDK oficial de MCP

Dimensión

mcp-agent-kit

SDK oficial de Python para MCP

Dependencias en tiempo de ejecución

0 (solo biblioteca estándar de Python)

18+ dependencias de terceros

Tamaño del paquete instalado

~15 KB

~42 MB (con dependencias transitivas)

Tiempo de arranque del proceso

< 1.2 ms

~120 ms

Superficie de auditoría

Archivo único / Cero CVEs

Múltiples dependencias de la cadena de suministro

Soporte de transporte

stdio + SSE

stdio + SSE


Inicio rápido

1. Crea un servidor MCP en 20 líneas

from mcp_agent_kit import MCPServer

app = MCPServer(name="my-agent-tools", version="1.0.0")

@app.tool(description="Calculate tax and total invoice amounts")
def calculate_invoice(subtotal: float, tax_rate: float = 0.07) -> dict:
    tax = round(subtotal * tax_rate, 2)
    return {"subtotal": subtotal, "tax": tax, "total": round(subtotal + tax, 2)}

if __name__ == "__main__":
    app.run_stdio()

Integración con Claude Desktop

Añade tu servidor directamente a claude_desktop_config.json:

{
  "mcpServers": {
    "my-tools": {
      "command": "python",
      "args": ["-m", "examples.simple_server"]
    }
  }
}

¡Reinicia Claude Desktop y tus herramientas personalizadas aparecerán inmediatamente en el cinturón de herramientas de Claude! 🔨


Características

  • Handshake MCP completo: initialize, notifications/initialized, ping.

  • Descubrimiento dinámico de herramientas: tools/list con generación automática de esquemas JSON.

  • Límites de error robustos: tools/call aísla las excepciones de tiempo de ejecución de las herramientas y devuelve mensajes de error estructurados al LLM sin bloquear el proceso stdio.

  • Cero dependencias: funciona sin configuración adicional en Python 3.10+.


Rigor de ingeniería y verificación

  • Pruebas: suite de pruebas pytest con un 100% de aprobación que cubre inicialización, descubrimiento de herramientas, validación de argumentos y manejo de excepciones.

  • Dependencias: 0 dependencias en tiempo de ejecución.

  • Autor: Reuben Bowlby — LinkedIn | Currículum


Escala empresarial

mcp-agent-kit forma parte del ecosistema de desarrollo HUMMBL. Para gobernanza multiagente de nivel empresarial (interruptores de apagado de 4 etapas, tokens de delegación firmados con HMAC y buses de auditoría criptográficos), visita github.com/hummbl-io.


Available Tools

2 tools
add_numbersA

Add two numbers together

NameRequiredDescriptionDefault
aYes
bYes

TDQS

A3.7/5.0
Behavior3/5

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

There are no annotations to carry the safety profile, so the description carries the burden. It clearly states the operation, and a pure addition has no obvious destructive or persistent side effects, but it does not explicitly mention that the result is returned or how errors are handled.

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. Every word contributes to the meaning without redundancy.

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 two-number addition tool with required parameters and no output schema, the description is minimally adequate. It could be more complete by explicitly stating that it returns the sum, but the operation is straightforward enough for an agent to infer the expected behavior.

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 0%, so the description must compensate. 'Add two numbers together' clarifies that both a and b are operands in the same addition, which adds meaning over bare names and types. It does not discuss edge cases like type coercion or numeric bounds, but these are minor for simple addition.

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 ('add') and resource ('two numbers'), making the tool's purpose immediately clear. It is also clearly distinct from the only sibling, get_system_info, since arithmetic and system information share no overlap.

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 usage context is implied: use this tool when a sum of two numbers is needed. However, the description gives no explicit guidance about when not to use it or any alternative, though the operation is elementary enough that confusion is unlikely.

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

get_system_infoA

Get host system architecture and OS details

NameRequiredDescriptionDefault

No parameters

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It correctly conveys that the operation is a read-like retrieval with no side effects, which is implicit in 'Get'. It does not detail the exact shape of the returned data (e.g., format, fields, possible failure modes), but for a simple system-info tool this is a moderate gap.

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 one short sentence and every word contributes to meaning. It is appropriately sized for the tool's simplicity, with no padding or redundant phrasing.

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 zero-parameter tool with no output schema, the description tells the agent what it will get ('host system architecture and OS details'). It does not specify the exact return format, but the tool's extreme simplicity and the clear scope make the description adequate. A slightly richer mention of OS/platform field names would push it to 5, but current wording is sufficient.

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

Parameters4/5

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

The tool has zero parameters, so the schema already fully describes the input surface with 100% coverage. The description adds the key behavioral context—what kind of information is returned—which is more than the schema alone. The baseline of 4 for zero-parameter tools 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 states a specific verb ('Get') and resource ('host system architecture and OS details'), and this distinguishes it clearly from the only sibling tool, add_numbers, which performs arithmetic. No ambiguity about what the tool returns.

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 the intended use case—retrieving system-level information—and no complex context is required for a zero-parameter tool. It does not explicitly state when not to use it, but with only one sibling that does something entirely unrelated, explicit exclusion is unnecessary.

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 observedadd_numbers
    • First observedget_system_info

TDQS

A3.8/5.0

Scored across 2 tools

Disambiguation5/5

The two tools are completely unrelated in purpose: one performs arithmetic and the other retrieves system information. There is no possibility of an agent confusing them.

Naming Consistency5/5

Both tools follow the same verb_noun snake_case pattern: add_numbers and get_system_info. Naming is predictable and consistent.

Tool Count3/5

With only two tools, the server sits at the low end of the acceptable range. The name mcp-agent-kit suggests a broader toolkit, so two tools feel thin.

Completeness2/5

The two tools do not form a coherent domain, and the server name implies a general-purpose agent kit, yet almost no common agent capabilities are represented. There are significant gaps if this is meant to be a functional toolkit.

Maintenance

ActivityMaintained
ResponsivenessSyncing

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