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Get Trust Summary

get_trust_summary

Read-onlyIdempotent

Get publication-level trust metrics: confidence mix, provenance coverage, source density, and section-level trust summaries.

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

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety and idempotency. The description adds value by specifying the kind of metrics returned, which is beyond the annotations. It does not mention any other behavioral traits like auth requirements or rate limits, but for a read-only tool with strong annotations, this is adequate.

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?

A single sentence that front-loads the core purpose ('publication-level trust metrics') and lists the specific outputs. Every word earns its place; no filler or redundancy. Perfectly sized.

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 read-only tool with no required parameters and an output schema provided, the description fully captures the tool's behavior. It enumerates the data returned, and the output schema handles return structure. No missing information an agent would need to invoke it correctly.

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 single optional parameter agent_id, which is exhaustively documented in the schema itself. The description adds no parameter-specific information, but the baseline is 3 when the schema carries the full load. No gap to compensate for.

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 states a specific verb ('Get'), a clear resource ('publication-level trust metrics'), and enumerates the exact data returned (confidence mix, provenance coverage, source density, section-level summaries). This distinguishes it from sibling tools like tat_stats or get_article, which cover different domains. The title reinforces the purpose without redundancy.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. Among many siblings (tat_stats, tat_expert_dashboard, get_related_articles), there is no mention of conditions, exclusions, or a preferred alternative. The description only states what it does, not when it should be chosen, leaving the agent to infer from the name.

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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