pytest
Runs pytest and returns structured test results (passed, failed, errors, skipped, failures).
This record as markdown: /tools/io-github-dave-london-github/pytest.md
What pytest does on Github
AI agents invoke pytest 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.
Why pytest is rated High
This tool runs pytest, which executes arbitrary Python code in the test suite. An AI agent could trigger execution of malicious or destructive test code, making this an Execute-category tool with high severity due to the broad blast radius of running arbitrary code.
From the tool's definition 'Runs pytest' — explicitly executes a test runner process on the host system
Attacks that exploit this kind of access
The rule that runs pytest safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Github, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For pytest, this is the rule to start with:
pytest 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Github, apply this rule, and every pytest call is checked against it from then on.
Questions about pytest
Runs pytest and returns structured test results (passed, failed, errors, skipped, failures). 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 pytest: 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.
pytest 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 pytest 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 pytest. 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.
pytest 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.
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