run_pipeline

Execute a Jarvis-CD pipeline end-to-end.

SERVERCLIO Adios SOURCEpypi:clio-kit

High RISK CLASS

Category Execute

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-iowarp-adios-mcp/run-pipeline.md

WHAT IT DOES

What run_pipeline does on CLIO Adios

AI agents invoke run_pipeline to trigger actions in CLIO Adios. 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.

RISK

Why run_pipeline is rated High

This tool runs arbitrary pipelines (Jarvis-CD) which can perform various computational and I/O operations. While the exact effects depend on pipeline configuration, the ability to execute an end-to-end pipeline on scientific HPC infrastructure (ADIOS2/BP5 data processing context) represents a high-severity Execute risk.

From the tool's definition The tool description explicitly states it will "Execute a Jarvis-CD pipeline end-to-end." The verb "Execute" combined with running an entire pipeline indicates this triggers external operations whose effects depend on arguments passed to the pipeline.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs run_pipeline safely

PolicyLayer is an MCP gateway: it sits between your AI agents and CLIO Adios, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For run_pipeline, this is the rule to start with:

run_pipeline Rate-limited

run_pipeline 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "run_pipeline": {
      "limits": [
        {
          "counter": "run_pipeline_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect CLIO Adios, apply this rule, and every run_pipeline call is checked against it from then on.

FAQ

Questions about run_pipeline

What does the run_pipeline tool do? +

Execute a Jarvis-CD pipeline end-to-end. It is categorised as a Execute tool in the CLIO Adios MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on run_pipeline? +

Register the CLIO Adios MCP server in PolicyLayer and add a rule for run_pipeline: 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 CLIO Adios. Nothing to install.

What risk level is run_pipeline? +

run_pipeline is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit run_pipeline? +

Yes. Add a rate_limit block to the run_pipeline 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.

How do I block run_pipeline completely? +

Set action: deny in the PolicyLayer policy for run_pipeline. 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.

What MCP server provides run_pipeline? +

run_pipeline is provided by the CLIO Adios MCP server (pypi:clio-kit). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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