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Get Related Articles

get_related_articles

Read-onlyIdempotent

Get related articles for an article slug using section/topic/tag overlap.

Input Schema

TableJSON Schema

Name	Required	Description
`slug`	Yes	Article slug
`limit`	No	Number of related articles (max 10)
`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.
`strategy`	No	Ranking strategy

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

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is fully covered. The description adds the overlap-based ranking method, which offers some behavioral context, but does not disclose additional details like rate limits or output structure. With annotations covering safety, this is adequate but not extensive.

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 sentence with no filler, directly stating the purpose and method. The core information is front-loaded, making it easy for an agent to quickly grasp the tool's function without parsing unnecessary text.

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

Completeness3/5

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

The tool has moderate complexity with 4 parameters and an output schema. The description covers the basic functionality but omits usage scenarios and any caveats about the strategy parameter or agent_id. Given the schema and annotations handle parameter details and safety, the description is minimally sufficient but lacks contextual guidance on when and how to apply the tool in a broader workflow.

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% – all four parameters (slug, limit, agent_id, strategy) have descriptions in the schema. The description itself adds no additional parameter semantics beyond what the schema already provides, so the baseline of 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?

The description clearly states the action ('Get related articles'), the resource ('an article slug'), and the method ('using section/topic/tag overlap'). This distinguishes it from siblings like get_article or search_articles, though it doesn't explicitly name them. The purpose is unambiguous and not a tautology.

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?

There is no guidance on when to use this tool versus alternatives such as search_articles or get_section_articles. The description provides no context about scenarios where related articles are preferred over other retrieval methods, leaving the agent to infer usage without explicit direction.

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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