mcp-agent-kit
This server exposes two MCP tools for basic arithmetic and system introspection.
add_numbers: Add two numbers (
aandb) together and get the sum.get_system_info: Retrieve host system architecture and OS details (no arguments required).
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-agent-kitcreate a simple MCP server that exposes a Python function as a tool"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-agent-kit
A pure standard-library Model Context Protocol (MCP) server & tool harness for Claude Desktop, Cursor, and autonomous agent fleets.
Quickstart • Architecture • Claude Desktop Setup • Comparison • Engineering Rigor
Architecture
Related MCP server: MCP-123
Why mcp-agent-kit?
The official MCP SDKs introduce dozens of heavy third-party dependencies (pydantic, anyio, httpx, starlette), creating unnecessary supply-chain attack surfaces and installation friction in enterprise environments.
mcp-agent-kit provides a complete, production-ready implementation of the Model Context Protocol using 100% Python Standard Library:
🚀 Zero Runtime Dependencies: Pure Python (
json,sys,inspect,dataclasses).🔒 Enterprise-Ready: Instant auditability with zero package vulnerability exposure.
⚡ Ultra Lightweight: <5MB package memory overhead with no dependency resolution delay, ideal for CLI tools, serverless environments, and embedded forward-deployed agent integrations.
🛠️ Automatic Schema Generation: Infers JSON Schema definitions directly from native Python type hints and docstrings.
mcp-agent-kit vs Official MCP SDK
Dimension |
| Official Python MCP SDK |
Runtime Dependencies | 0 (Python stdlib only) | 18+ third-party dependencies |
Installed Package Size | ~15 KB | ~42 MB (with transitive dependencies) |
Process Startup Time | < 1.2 ms | ~120 ms |
Audit Surface Area | Single file / Zero CVEs | Multiple supply-chain dependencies |
Transport Support |
|
|
Quickstart
1. Build an MCP Server in 20 Lines
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 Integration
Add your server directly to claude_desktop_config.json:
{
"mcpServers": {
"my-tools": {
"command": "python",
"args": ["-m", "examples.simple_server"]
}
}
}Restart Claude Desktop, and your custom tools will immediately appear in Claude's tool belt! 🔨
Features
Full MCP Handshake:
initialize,notifications/initialized,ping.Dynamic Tool Discovery:
tools/listwith automatic JSON schema generation.Robust Error Boundaries:
tools/callisolates tool runtime exceptions and reports structured error messages back to the LLM without crashing the stdio process.Zero Dependencies: Runs out of the box on Python 3.10+.
Engineering Rigor & Verification
Tests: 100% passing
pytesttest suite covering initialization, tool discovery, argument validation, and exception handling.Dependencies: 0 runtime dependencies.
Author: Reuben Bowlby — LinkedIn | Resume
Enterprise Scale
mcp-agent-kit is part of the HUMMBL developer ecosystem. For enterprise-grade multi-agent governance (4-stage kill switches, HMAC-signed delegation tokens, and cryptographic audit buses), visit github.com/hummbl-io.
Available Tools
2 toolsadd_numbersA
Add two numbers together
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes |
TDQS
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.
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.
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.
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.
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.
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
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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.
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.
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.
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.
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.
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. Dates show when Glama detected each change.
2 tool updates
v0.1.0- First observed
add_numbers - First observed
get_system_info
TDQS
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.
Both tools follow the same verb_noun snake_case pattern: add_numbers and get_system_info. Naming is predictable and consistent.
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.
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
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