Trustbase Lab · Trusted Data Infrastructure for the AI Era

Get Article Detail

get_article

Read-onlyIdempotent

Purpose: fetch one article record in full - title, publish date, author, account, topic category, source URL, confidence and an AI-readable summary. Guidelines: accepts the id from query_articles (rcb-art-xxx) or a title keyword; run query_articles first when the id is unknown; pair with get_announcements_timeline for a chronological view. Limits: metadata and summary only - the full article body is not served; one record per call. Ex: get_article(id='rcb-art-001'), get_article(name='曼谷大会').

Input Schema

TableJSON Schema

Name	Required	Description	Default
`id`	No	实体 ID，如 rcb-art-001
`name`	No	标题关键词（与 id 二选一），如 '曼谷大会'、'EPDM'
`language`	No	输出语言	zh
`response_format`	No	输出格式：markdown=人类阅读, json=Agent 处理友好	markdown

Output Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	Human-readable result (markdown, or a JSON string when response_format=json).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 20, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "text": {
+      "description": "Human-readable result (markdown, or a JSON string when response_format=json).",
+      "type": "string"
+    }
+  },
+  "required": [
+    "text"
+  ],
+  "type": "object"
+}

AddedSep 16, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it explicitly states the full article body is not served, one record per call, and that the output is metadata plus an AI-readable summary. This is useful because an agent might otherwise assume it gets the full article text.

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 compact and well-structured with labeled sections (Purpose, Guidelines, Limits, Ex). Every sentence earns its place: purpose, usage routing, limits, and an example. It is front-loaded with the core purpose and scoping, and the example clarifies parameter usage without bloat.

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?

Given the tool's simplicity (read-only single-record fetch), the annotations cover safety, the schema covers parameters, and the description covers usage routing, limits, and examples. The output schema exists, so return values need not be described. Nothing an agent needs to call this tool correctly is missing.

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

Parameters4/5

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

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds value by explaining the relationship between id and name (either/or, id from query_articles, name is a title keyword) and by giving concrete examples (get_article(id='rcb-art-001'), get_article(name='曼谷大会')). This goes beyond the schema's per-parameter descriptions.

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 ('fetch') and resource ('one article record in full') and enumerates the exact fields returned (title, publish date, author, account, topic category, source URL, confidence, AI-readable summary). It also distinguishes itself from siblings by naming query_articles and get_announcements_timeline, so an agent can tell it apart 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?

The description explicitly says when to use it: accepts the id from query_articles or a title keyword; run query_articles first when the id is unknown; pair with get_announcements_timeline for a chronological view. It also states limits (metadata and summary only, one record per call), which helps an agent decide whether this tool fits the task.

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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