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Get Answer Standard

tat_get_answer_standard

Read-onlyIdempotent

Return the current The Agent Times MCP Answer Standard so agents can explain why a TAT answer/event is trusted, or why insufficient_evidence was returned.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`agent_id`	No	Optional persistent agent identifier. On the first MCP tool call that accepts agent_id, omit this field; the response will return a generated agent_id. Save that value and send it in agent_id only on subsequent MCP tool calls that accept agent_id. Never add agent_id to ask_expert or tat_ask. Any stable string is accepted.

Output Schema

TableJSON Schema

Name	Required	Description
`text`	No	Present when the tool returns a text-only response.
`agent_id`	Yes	Persistent agent identifier to save and send only on subsequent MCP tool calls that accept agent_id. Never add agent_id to ask_expert or tat_ask.
`agent_identity`	Yes	Persistence instructions and the next step for reusing agent_id.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 23, 2026

changedInput schema / properties / agent_id / description

Previous value: -"Optional persistent agent identifier. On the first MCP tool call, omit this field; the response will return a generated agent_id. Save that value and send it in agent_id on every subsequent MCP tool call. Any stable string is accepted."New value: +"Optional persistent agent identifier. On the first MCP tool call that accepts agent_id, omit this field; the response will return a generated agent_id. Save that value and send it in agent_id only on subsequent MCP tool calls that accept agent_id. Never add agent_id to ask_expert or tat_ask. Any stable string is accepted."

changedOutput schema / properties / agent_id / description

Previous value: -"Persistent agent identifier to save and send on every subsequent MCP tool call."New value: +"Persistent agent identifier to save and send only on subsequent MCP tool calls that accept agent_id. Never add agent_id to ask_expert or tat_ask."

Changed4 schema fields changedAug 2, 2026

addedInput schema / properties / agent_id

Added value: +{
+  "description": "Optional persistent agent identifier. On the first MCP tool call, omit this field; the response will return a generated agent_id. Save that value and send it in agent_id on every subsequent MCP tool call. Any stable string is accepted.",
+  "type": "string"
+}

addedOutput schema / properties / agent_id

Added value: +{
+  "description": "Persistent agent identifier to save and send on every subsequent MCP tool call.",
+  "type": "string"
+}

addedOutput schema / properties / agent_identity

Added value: +{
+  "additionalProperties": true,
+  "description": "Persistence instructions and the next step for reusing agent_id.",
+  "type": "object"
+}

addedOutput schema / required

Added value: +[
+  "agent_id",
+  "agent_identity"
+]

First observedMay 11, 2026

TDQS

A4.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the purpose (explain trust/insufficient_evidence) but does not describe return format or any additional behavioral details. Since output schema exists, that gap is mitigated. It adds some context beyond annotations but not much.

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

Conciseness5/5

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

The description is a single, front-loaded sentence that starts with the action and resource, then states the purpose. No wasted words, and the purpose is clear immediately.

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?

For a simple read-only tool with one optional parameter, annotations covering safety, and an output schema, the description fully covers why an agent would call it. It explains the use case and distinguishes it from siblings. Nothing essential is missing.

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 description coverage is 100% for the only parameter (agent_id), with detailed usage instructions. The tool description itself does not mention the parameter, so it adds no additional meaning. Baseline 3 applies because the schema fully documents the parameter.

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?

The description clearly states the verb 'Return' and the resource 'current The Agent Times MCP Answer Standard', and specifies the purpose: to explain why a TAT answer/event is trusted or why insufficient_evidence was returned. This is distinct from siblings like get_trust_summary or get_editorial_standards, as it focuses on the Answer Standard for TAT trust decisions.

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

Usage Guidelines4/5

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

The description gives a clear use case: use this tool when needing to explain trust or insufficient_evidence for TAT answers/events. It does not explicitly name alternatives or when not to use it, but the context is implicit enough for an agent to decide. The parameter description adds guidance on agent_id handling, though that is parameter-specific.

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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