plan_query

SERVERMcp Server SOURCEhttps://dchub.cloud/mcp

Medium RISK CLASS

Category Write

Parameters 21 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/cloud-dchub-mcp-server/plan-query.md

WHAT IT DOES

What plan_query does on Mcp Server

AI agents use plan_query to create or update resources in Mcp Server, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Mcp Server environment.

Parameter	Type	Required	Description
`intent`	string	Yes	Natural-language description of what you are trying to find out, e.g. "rank markets for a 200MW AI campus" or "how much power is available in ERCOT"
`context`	object	—	Optional structured hints: {lat, lon, iso, market, capacity_mw, candidate_id, state (2-letter), since} — sharpens args_hint values and routing (e.g. lat/lon boo

Parameters from the server's own tool schema.

RISK

Why plan_query is rated Medium

An AI agent can call plan_query faster than any human can review: one bad instruction and it creates or modifies resources in Mcp Server by the hundred, each call as confident as the last.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs plan_query safely

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

plan_query Rate-limited

plan_query stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "plan_query": {
      "limits": [
        {
          "counter": "plan_query_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

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

FAQ

Questions about plan_query

What does the plan_query tool do? +

INSPECT-ONLY — returns the plan WITHOUT running it. For a real multi-step DC Hub question call execute_plan(intent="...") instead: it uses the SAME deterministic no-LLM planner and then RUNS the sequence server-side, returning the answers in one envelope. Reach for plan_query only to review, log, diff or audit a plan before executing it yourself. Deterministic keyword routing over the tool registry — no LLM, no network, same intent always returns the same plan (free). Returns _entity=query_plan {best_tool, intent_confidence + workflow_confidence (dual 0-1: question-read vs executability), reason, planner_rationale, recommended_sequence:[{step, tool, depends_on, estimated_calls, why, args_hint}], execution_waves (steps grouped into concurrency waves), execution_strategy.parallel_groups, execution_estimate {estimated_calls, estimated_latency_ms, parallelizable}, alternatives (each with when + rejected_because), coverage_notes, matched_classes} plus a versioned replay (schema_version 1): planner_version, decisions:[{id, step, kind, status, decision, rationale, decision_confidence, depends_on}], rejected:[{id, tool, reason}], execution_graph:{waves, parallel_groups} — auditable and machine-readable, safe to log and diff across versions. args_hint values in <angle brackets> come from the named earlier step — substitute them, never invent them. Pass structured hints via context (lat/lon, iso, market, capacity_mw, candidate_id, state, since) to sharpen the plan. For a family-level browse use discover_tools. This tool plans — it never executes; tools/list stays canonical for schemas. It is categorised as a Write tool in the Mcp Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does plan_query accept? +

plan_query accepts 2 parameters: intent, context. Required: intent. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on plan_query? +

Register the Mcp Server MCP server in PolicyLayer and add a rule for plan_query: 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 Mcp Server. Nothing to install.

What risk level is plan_query? +

plan_query is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit plan_query? +

Yes. Add a rate_limit block to the plan_query 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 plan_query completely? +

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

plan_query is provided by the Mcp Server MCP server (https://dchub.cloud/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

More on Mcp Server, and thousands of servers like it.

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emit_variant_plan on 0sec →team_manager_plan_room on Team Manager MCP → The MCP Attack Database →

Guides

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