Runs a build command and returns structured success/failure with errors and warnings.
AI agents invoke build to trigger actions in Github. 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 build commands with effects that depend on the specific build system and arguments provided. While not destructive by itself, build execution can consume resources, trigger deployments, or invoke side effects in CI/CD pipelines. Classified as Execute rather than Write because it runs external commands/operations rather than directly creating/modifying versioned data.
From the tool's definition Tool description states 'Runs a build command' which triggers external operations whose effects depend on arguments. The output structure (success/failure with errors and warnings) confirms execution semantics rather than data retrieval.
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 Github, 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 Github MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Github 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 Github. 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 Github MCP server (@paretools/github). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Github, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
202 Github tools catalogued and risk-classified — across an index of 43,000+ MCP servers.