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format_thread

Split long text into numbered Twitter threads with proper character limits for social media sharing.

Instructions

Split long text into a numbered X (Twitter) thread (1/x).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe full text content
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It describes the basic behavior (splitting text into a thread) but lacks details on constraints like character limits per thread part, handling of formatting or media, error conditions, or rate limits. This is a significant gap for a tool that modifies text output.

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, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates what the tool does, making it easy to understand at a glance.

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

Completeness2/5

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

Given the complexity of text processing and lack of annotations or output schema, the description is incomplete. It doesn't explain the thread format details (e.g., numbering style, length limits), return values, or error handling. This leaves gaps for an AI agent to use the tool effectively.

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?

The input schema has 100% description coverage, with one parameter 'text' documented as 'The full text content.' The description adds no additional meaning beyond this, such as examples or formatting requirements. Baseline 3 is appropriate since the schema handles the parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Split long text into a numbered X (Twitter) thread (1/x).' It specifies the action (split), the resource (long text), and the output format (numbered X thread). However, it doesn't differentiate from sibling tools like 'audit_copy' or 'generate_hooks', which appear unrelated but no explicit distinction is made.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions the output format but doesn't specify scenarios, prerequisites, or exclusions. Without context on sibling tools, users must infer usage based on the purpose alone.

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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