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agent_query
Read-onlyIdempotent

Run a question through an agent against a real conversation, to see how it would answer. Returns the answer without sending anything — nothing reaches the prospect, which makes this the safe way to vet an agent before it goes active. For what it has already said use agent_logs. This is not a way to reply to anyone: replying by hand is message_send, and only on an explicit yes. Requires the "Agents" permission on the plan.

Input Schema

NameRequiredDescriptionDefault
queryYesThe question to put to the agent.
agentIdYesAI agent id, from `agent_list`.
conversationIdYesContext for the retrieval.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive, but the description adds meaningfully beyond them: the no-delivery guarantee ('nothing reaches the prospect') and the plan-level 'Agents' permission requirement — both are behavioral facts not encoded in annotations. It doesn't describe what the returned answer looks like or latency/token behavior, so not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action, then return behavior, disambiguation, and permissions in that order — every sentence carries distinct information. Slightly dense with three sibling references in one paragraph, which costs a point on structure but not clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description states that the answer is returned and that nothing is sent — exactly what an agent needs to interpret the result. Params are fully schema-described, permissions are named, and alternatives are routed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and all three required params are documented in the schema (`query`, `agentId` with a pointer to `agent_list`, `conversationId` as context). The description adds no param-level detail beyond that, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource — run a question through an agent against a real conversation — and immediately frames the purpose (preview how it would answer). It explicitly distinguishes itself from `agent_logs` (past output) and `message_send` (actual replying), so the agent can place it among siblings without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives the when (vetting an agent before it goes active), the when-not (not a way to reply; replying by hand is `message_send`, and only on an explicit yes), and the alternative for historical output (`agent_logs`). Routing is explicit rather than implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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