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seotrader

SpeedContent Social MCP

by seotrader

Generate a social media post

generate_social_post

Write social media posts for seven platforms from a topic, matched to each platform's conventions, optionally scored for AI detection and paired with a generated image.

Instructions

Write a social media post for a given platform and topic. The post is written to that platform's conventions, rewritten to read as human-authored, optionally scored against AI detection, and optionally illustrated with a generated image. Blocks until generation finishes, typically 20-90 seconds. Costs credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toneNoDefaults to friendly.
topicYesWhat the post should be about.
languageNoOutput language by name. Defaults to English.
platformYesTarget platform. Each has its own style guide and default length.
quantityNoHow many distinct variations to generate. Defaults to 1.
detect_aiNoScore the post against AI detection and retry if it reads as machine-written. Adds 8 credits per post. Omit for automatic: runs only at 150+ words, because short posts score unreliably.
brand_nameNoBrand to mention where it reads naturally.
brand_toneNoVoice guidance, e.g. 'Direct, no jargon'.
word_countNoTarget length per post. Omit to use the platform default (LinkedIn 100, YouTube 150, Facebook/Pinterest 75, Instagram/TikTok 50, X 40).
brand_audienceNoWho the post should speak to.
brand_keywordsNoComma-separated terms to work in.
generate_imageNoGenerate a matching image. Defaults to true. Adds 10 credits per post.
brand_descriptionNoWhat the business does.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
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 a good job: it discloses blocking behavior with a concrete latency range (20-90s), that credits are consumed, and the non-obvious side effect that output is rewritten to read as human-authored. It does not cover auth requirements or error behavior, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four tight sentences with zero filler, front-loading what the tool does before the behavioral facts (blocking time, cost). Efficient, though the final 'Costs credits' sentence is terse enough that it could carry more cost detail.

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 13-parameter generation tool with no output schema and no annotations, the description covers the essentials an agent needs: latency, blocking semantics, credit cost, and the multi-step pipeline. The main gap is that it never indicates what is returned (single post vs. quantity variations with scores/images), which an agent might want given no output schema.

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 all 13 parameters, including defaults and platform word counts. The description adds no parameter-level detail beyond the summary sentence, so the baseline 3 is appropriate.

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?

States a specific verb and resource (write a social media post) scoped to a platform and topic, and enumerates the pipeline steps (platform conventions, human-rewrite, AI detection, image). It is distinguishable from check_social_job and list_social_platforms by name and intent, though it never explicitly names those siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (generate a post for a platform/topic) and adds two useful operating facts — it blocks until completion and costs credits. However, it never states when to prefer this tool over check_social_job or how it relates to an async job flow, so the routing guidance is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.