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conorbronsdon

Social PostLint-MCP

check_post

Verify a social post against a platform's actual character limit before publishing. Get the counted length, allowed limit, and the factors that affect the count under that platform's rules.

Instructions

Check a social post against one platform's real character limit and report whether it fits. Returns the counted length, the limit, and what drove the count (URLs billed at a fixed width, non-Latin characters billed double, emoji sequences collapsed to one grapheme). Use this before publishing anything with a hard limit — the counting rules are per-platform and are not text.length.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe post text, exactly as it will be published. Leading and trailing whitespace is trimmed, as the platforms do. `[URL]`-style placeholders are priced as a real link.
platformYesWhich platform's rules to apply: x, x_premium, bluesky, linkedin, threads, mastodon, discord.
Behavior5/5

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

With no annotations, the description carries the full burden and delivers: it reveals non-obvious counting rules (URLs at fixed width, non-Latin characters double, emoji as one grapheme) and warns that results differ from text.length. This gives the agent valuable insight into the tool's behavior beyond basic function.

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, no fluff. The most important action is first, followed by return value details and a practical usage tip. Every sentence contributes.

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?

The description covers the tool's purpose, return value, counting rules, and usage context. It lacks exact output structure, but with no output schema, the high-level return description is adequate for an agent to understand what to expect. It's complete enough for most scenarios.

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 schema already covers 100% of parameters with meaningful descriptions (trimming, placeholder pricing, platform enum). The tool description adds little parameter-specific info beyond the schema, so the baseline of 3 applies.

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 opens with a specific verb and resource: 'Check a social post against one platform's real character limit and report whether it fits.' It clearly states the core function and differentiates itself from siblings by emphasizing 'one platform' (vs check_post_all) and the idea of limits (vs platform_limits).

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?

It gives an explicit usage invitation: 'Use this before publishing anything with a hard limit.' However, it does not explicitly name alternatives or state when not to use it, though the sibling tool names (check_post_all, platform_limits) imply some distinctions.

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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