run_select_parallel

A execute tool on the Mcpg MCP server.

SERVERMcpg SOURCEpypi:mcpg

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-devopam-mcpg/run-select-parallel.md

WHAT IT DOES

What run_select_parallel does on Mcpg

AI agents invoke run_select_parallel to trigger actions in Mcpg. 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_select_parallel is rated High

The name suggests executing parallel SELECT queries against a PostgreSQL database. While SELECT is typically read-only, 'run_' prefix indicates execution of database operations, and without a description we cannot confirm it doesn't permit side-effecting SQL. The sibling tools (analyze_*, add_*) suggest this server runs active database operations.

From the tool's definition Tool name 'run_select_parallel' and server context as a PostgreSQL MCP server; description is empty and uninformative.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs run_select_parallel safely

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

run_select_parallel Rate-limited

run_select_parallel 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_select_parallel": {
      "limits": [
        {
          "counter": "run_select_parallel_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 Mcpg, apply this rule, and every run_select_parallel call is checked against it from then on.

FAQ

Questions about run_select_parallel

What does the run_select_parallel tool do? +

run_select_parallel is a execute tool on the Mcpg MCP server. It is categorised as a Execute tool in the Mcpg 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_select_parallel? +

Register the Mcpg MCP server in PolicyLayer and add a rule for run_select_parallel: 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 Mcpg. Nothing to install.

What risk level is run_select_parallel? +

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

Can I rate-limit run_select_parallel? +

Yes. Add a rate_limit block to the run_select_parallel 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_select_parallel completely? +

Set action: deny in the PolicyLayer policy for run_select_parallel. 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_select_parallel? +

run_select_parallel is provided by the Mcpg MCP server (pypi:mcpg). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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