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by hummbl-dev

mcp-agent-kit

Python Version Runtime Dependencies Protocol License

一个纯标准库实现的 Model Context Protocol(MCP)服务器与工具平台,适用于 Claude Desktop、Cursor 和自主智能体集群。

快速开始 • 架构 • Claude Desktop 设置 • 对比 • 工程严谨性


架构


Related MCP server: MCP-123

为什么选择 mcp-agent-kit?

官方 MCP SDK 引入了数十个重量级第三方依赖(pydantic、anyio、httpx、starlette),在企业环境中造成了不必要的供应链攻击面和安装阻力。

mcp-agent-kit 使用 100% Python 标准库,提供了 Model Context Protocol 的完整、生产就绪实现:

  • 🚀 零运行时依赖:纯 Python(json、sys、inspect、dataclasses)。

  • 🔒 企业级就绪:即时可审计,零软件包漏洞暴露。

  • ⚡ 超轻量级:亚毫秒级启动时间,非常适合 CLI 工具、无服务器环境以及嵌入式前置部署的智能体集成。

  • 🛠️ 自动生成 Schema:直接从原生 Python 类型提示和 docstring 推断 JSON Schema 定义。


mcp-agent-kit 对比官方 MCP SDK

维度

mcp-agent-kit

官方 Python MCP SDK

运行时依赖

0(仅 Python 标准库)

18+ 个第三方依赖

安装包体积

~15 KB

~42 MB(含传递依赖)

进程启动时间

< 1.2 ms

~120 ms

审计面

单文件 / 零 CVE

多个供应链依赖

传输支持

stdio + SSE

stdio + SSE


快速开始

1. 用 20 行代码构建一个 MCP 服务器

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()

Claude Desktop 集成

将你的服务器直接添加到 claude_desktop_config.json:

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

重启 Claude Desktop,你的自定义工具将立即出现在 Claude 的工具腰带中!🔨


功能特性

  • 完整的 MCP 握手:initialize、notifications/initialized、ping。

  • 动态工具发现:tools/list,自动生成 JSON schema。

  • 健壮的错误边界:tools/call 可隔离工具运行时异常,并向 LLM 返回结构化错误信息,而不会导致 stdio 进程崩溃。

  • 零依赖:在 Python 3.10+ 上开箱即用。


工程严谨性与验证

  • 测试:pytest 测试套件 100% 通过,涵盖初始化、工具发现、参数验证和异常处理。

  • 依赖:0 个运行时依赖。

  • 作者:Reuben Bowlby — LinkedIn | 简历


企业级规模

mcp-agent-kit 是 HUMMBL 开发者生态系统的一部分。如需企业级多智能体治理(4 阶段终止开关、HMAC 签名的委派令牌和加密审计总线),请访问 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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