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emit_assignment_prompt

Generates the LLM prompt for assigning papers to proposed sub-topics during the topic build flow.

Instructions

Build the topic-build Phase 2 (sub-topic assignment) LLM prompt.

Part of the multi-phase topic build flow: Phase 1 proposes sub-topics for a cluster; Phase 2 (this tool) emits the prompt that asks an LLM to assign each paper to one of those sub-topics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_slugYesthe cluster being organised.
subtopicsYeslist of dicts matching ``research_hub.topic.SubtopicProposal`` — i.e. ``{"slug": str, "title": str, "description": str}`` (``description`` optional, defaults to ``""``).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations provided, so description must disclose behavioral traits. It only states it emits a prompt, lacking information on side effects, idempotency, or authorization needs. Output schema exists but description doesn't reference it.

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?

Two sentences plus a one-line clarification. Front-loaded with purpose, no extraneous content. Highly efficient.

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?

Describes the role in the topic-build flow and mentions the output is an LLM prompt. However, it does not specify prerequisites (e.g., completed Phase 1) or how to use the resulting prompt. Adequate but gaps remain.

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 coverage is 100% with clear descriptions for both parameters. The description does not add meaning beyond what the schema provides, so baseline score of 3 is appropriate.

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 it builds the Phase 2 prompt for sub-topic assignment, distinguishing it from sibling tools like propose_subtopics (Phase 1). Verb 'emit' + 'prompt' is specific.

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?

Context clarifies it's part of a two-phase flow, but does not explicitly exclude uses outside that flow or mention when not to use it. No alternative tools named.

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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