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GigSoul x402

generate-linkedin-post

Write one LinkedIn post on a topic: post text, hashtags, and an opening hook. Use for a single original post. To adapt an existing article for LinkedIn and other channels at once, use repurpose-long-form. To score a draft before posting, use predict-viral-potential. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoThe topic or point the post should make. Example: Why we stopped cold calling and what replaced it
contextNoOptional: author role, audience, or a story to include.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / context / description
      Previous value: -"Optional supporting text or content to analyze"New value: +"Optional: author role, audience, or a story to include."
    • changedInput schema / properties / query / description
      Previous value: -"The question or input for this tool. Example: why AI agents need a payment rail"New value: +"The topic or point the post should make. Example: Why we stopped cold calling and what replaced it"
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well by disclosing the pay-per-call cost, the required payment-signature header, and the error behavior when it is missing. It stops short of describing the exact response format, but the output contents are at least partially listed.

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?

Three sentences, each earning its place: purpose, usage routing, and payment behavior. The most important info is front-loaded and there is no filler or redundancy.

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

Completeness4/5

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

For a simple two-parameter generation tool, the description covers purpose, scope, alternatives, and payment requirements. The only notable gap is the absence of a detailed return structure, but the listed deliverables (text, hashtags, hook) mitigate that.

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%, so the schema already documents both parameters well. The description adds only a generic sense of 'topic' and 'context,' which is helpful but not a significant increase over the schema's own examples.

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 uses a specific verb-resource pair ('Write one LinkedIn post') and names the deliverables: post text, hashtags, and an opening hook. It also distinguishes itself from nearby siblings by explicitly limiting scope to a 'single original post.'

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 states exactly when to use this tool and names two alternatives with their conditions: repurpose-long-form for adapting an article, and predict-viral-potential for scoring a draft. This leaves no ambiguity about routing.

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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