Runs a build command and returns structured success/failure with errors and warnings.
AI agents invoke build to trigger actions in Python. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool executes a build command whose effects depend on the build system configuration and arguments provided. While the tool itself only reports success/failure/errors/warnings (structured output), the underlying action of 'running' a build command means executing external code whose side effects are not directly controlled by the tool but by the build system invoked.
From the tool's definition Tool description states it 'Runs a build command' - the word 'Runs' indicates execution of external operations. Build commands can compile code, invoke arbitrary scripts, and have side effects determined by build configuration arguments.
Documented attack patterns abuse exactly the kind of access build gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Python, and nothing reaches the server without passing your rules. This is the rule we recommend for build:
{
"version": "1",
"default": "deny",
"tools": {
"build": {
"limits": [
{
"counter": "build_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}build stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Runs a build command and returns structured success/failure with errors and warnings. It is categorised as a Execute tool in the Python MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Python MCP server in PolicyLayer and add a rule for build: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Python. Nothing to install.
build is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the build rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for build. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
build is provided by the Python MCP server (Dave-London/Pare). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Python, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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202 Python tools catalogued and risk-classified — across an index of 43,000+ MCP servers.