Get the configuration files (.woodpecker.yml) used by a specific pipeline. Essential understanding pipeline setup. If required information is not provided, use the git command or any available tool to get more context about the current commit, the repository, or the author of the commit.
AI agents call get_pipeline_config to retrieve information from Woodpecker without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and returns pipeline configuration data without any side effects. While it may potentially expose sensitive configuration details (which could inform other attacks), the tool itself performs no write, delete, or execution operations. The note about using git commands or available tools is instructional context, not a description of destructive capability.
From the tool's definition Tool 'get_pipeline_config' retrieves configuration files (.woodpecker.yml) used by a specific pipeline. The description explicitly states it gets/retrieves configuration, with no mention of modifying, deleting, or executing operations.
Documented attack patterns abuse exactly the kind of access get_pipeline_config gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Woodpecker, and nothing reaches the server without passing your rules. This is the rule we recommend for get_pipeline_config:
{
"version": "1",
"default": "deny",
"tools": {
"get_pipeline_config": {}
}
}get_pipeline_config is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get the configuration files (.woodpecker.yml) used by a specific pipeline. Essential understanding pipeline setup. If required information is not provided, use the git command or any available tool to get more context about the current commit, the repository, or the author of the commit. It is categorised as a Read tool in the Woodpecker MCP Server, which means it retrieves data without modifying state.
Register the Woodpecker MCP server in PolicyLayer and add a rule for get_pipeline_config: 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 Woodpecker. Nothing to install.
get_pipeline_config is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_pipeline_config 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 get_pipeline_config. 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.
get_pipeline_config is provided by the Woodpecker MCP server (j04n-f/woodpecker-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Woodpecker, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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7 Woodpecker tools catalogued and risk-classified — across an index of 43,000+ MCP servers.