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MindThread MCP

Generate post drafts

generate_posts

Generates 1–5 Threads post drafts from a topic, account persona, optional reference context, and quality level; returns rule violations without publishing or queuing.

Instructions

Generates 1 to 5 post drafts about a topic using a content formula (auto, or an id from list_formulas), the account persona and material, optional reference context (up to 8,000 characters) and a quality level (fast, standard, premium). Nothing is published or queued. The result carries rule_violations against the account's content_rules. Applies when the user wants drafts to read, edit or choose from.

Input Schema

NameRequiredDescriptionDefault
countNo1-5
topicYesWhat to write about
contextNoReference material up to 8000 chars
formulaNoauto or a formula id from list_formulas
qualityNofast | standard | premium
account_idNoPersona source

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnly false, destructive false, closed-world, non-idempotent), and the description adds real context beyond them: no publish/queue side effect, the 8,000-character context ceiling, quality tiers, and that the result surfaces rule_violations against account content_rules. It stops short of saying where drafts are persisted or whether premium quality has latency/cost implications for a non-idempotent call.

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 dense sentences, front-loaded with what is generated and how much, then constraints, then the use-case trigger. Every clause carries information; no restatement of the title.

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?

Six parameters with no output schema, and the description does cover the return-side hint (rule_violations). The remaining gap is where drafts land and whether they must be added to the library or queued separately — a material detail given the surrounding add_library and bulk_queue_library siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description earns above it by binding formula to a discovery tool ('auto, or an id from list_formulas') and restating quality tiers and the context limit in prose. It adds no enum constraint the schema lacks, but the cross-tool linkage for formula is genuinely useful.

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?

Specific verb (generates) plus resource (post drafts) with an explicit 1-5 count range and the exact inputs that shape the output. It also carves out the negative space — 'Nothing is published or queued' — which separates it from publish, schedule_text and bulk_queue_library in the sibling list.

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?

Gives a clear triggering condition: 'Applies when the user wants drafts to read, edit or choose from,' and routes the formula id lookup to list_formulas. It does not name the write-path alternatives (publish, schedule_text) explicitly, so the exclusion is inferred rather than stated.

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