Agent News by The Agent Times

Expert Research Dashboard

tat_expert_dashboard

Read-onlyIdempotent

Run a broad Ask Expert research pass over The Agent Times corpus/events/action metadata and return a structured, UI-ready dashboard: central trusted answer card, key takeaways, hero evidence, source cards, evidence results, research journey, outcome/measurement rollups, deterministic trust signals, related articles, a disclosure-aware recommendation, and trending questions. Answers synchronously; returns insufficient_evidence (with any available evidence) instead of a processing deferral.

Input Schema

TableJSON Schema

Name	Required	Description
`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.
`question`	Yes	English question about the agent economy or TAT coverage
`max_results`	No	Candidate pool size (1-12, default 10)
`max_sources`	No	Maximum source budget (1-20, default 8)
`source_agent`	No	Calling agent identifier

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"
+]

AddedJul 3, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already carry readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the bar is lower. Beyond that, the description adds genuinely useful behavior: 'Answers synchronously', and the distinctive fallback 'returns insufficient_evidence (with any available evidence) instead of a processing deferral' — plus disclosure-awareness and deterministic trust signals. No contradiction with annotations.

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?

Purpose is front-loaded, the behavioral clause ('Answers synchronously... instead of a processing deferral') is deferred to the end, and the ~12-component enumeration is dense but informative for an agent predicting output. Slightly long, yet every element adds content — no obvious waste.

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

Completeness4/5

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

For a complex dashboard tool with an output schema present, the description is thorough: it enumerates all output sections, declares synchronous behavior, and covers the insufficient-evidence edge case. The only meaningful gap is explicit routing guidance relative to the ask_expert/tat_ask siblings.

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%, so the baseline is 3. The description only weakly maps to parameters — 'research pass' aligns conceptually with max_results (candidate pool) and max_sources (source budget), and the question is implied — but it does not add syntax or format detail beyond the schema. The schema does the heavy lifting.

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+resource: 'Run a broad Ask Expert research pass over The Agent Times corpus/events/action metadata' and a concrete outcome: 'a structured, UI-ready dashboard'. The qualifier 'broad... Ask Expert research pass' against siblings ask_expert and tat_ask clearly positions it as the dashboard-wide variant rather than a narrow single answer, so an agent can distinguish it without opening the schema.

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 context is clear — this is for a broad research pass producing a synthesized dashboard — and the phrase 'Ask Expert research pass' ties it to the research family. However, it never names the natural alternatives (ask_expert, tat_ask) or states when to prefer them (e.g., narrow question → ask_expert, broad synthesis → this). Clear context with no explicit exclusions.

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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