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Social media character counter

text_social_count
Read-onlyIdempotent

Check post text against platform character limits with platform-specific counting, plus words, hashtags, mentions, links, emoji, and SMS encoding details.

Instructions

Check a post against platform limits the way the platforms count: X/Twitter weighted length (links = 23, emoji = 2, CJK = 2; limit 280), Threads 500, Bluesky 300, Instagram 2,200 (+ 30 hashtags), LinkedIn 3,000, Facebook 63,206, TikTok 4,000, YouTube title 100 / description 5,000, Pinterest 500, Google title ~60, meta description ~160. Characters are counted as user-perceived graphemes (one emoji = 1). Also: words, hashtags, mentions, links, emoji, and SMS encoding (GSM-7 vs Unicode) with the number of SMS parts and the characters that force Unicode. Give "text" or a text file "path". Web version: https://s3eidat.github.io/social-counter/

Input Schema

NameRequiredDescriptionDefault
pathNoOr: a path to a UTF-8 text file.
textNoThe post text.
platformsNoOnly these meters: x, threads, bsky, ig, li, fb, tt, yt, ytd, pin, gt (Google title), gd (meta description). Default: all.
gsmLookalikesNoAlso return the text with curly quotes, dashes, ellipses, odd spaces etc. replaced by GSM-7 look-alikes (cheaper SMS).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already establish read-only and idempotent behavior, and the description adds substantial detail: platform-specific weighted counting, grapheme rules, SMS GSM-7 vs Unicode segmentation, default-all platforms, and file input. There is no contradiction with the annotations.

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?

The description is dense but front-loads the core check, and most details about limits and SMS encoding directly support invocation. The web-version URL is dispensable and the platform list is lengthy, keeping it from a 5.

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?

Covers inputs, optional platform selection, and the metrics returned, which is important because there is no output schema. It does not specify output structure or what happens when neither text nor path is supplied, leaving a small gap.

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, but the description adds useful meaning by listing exact platform limits and explaining what gsmLookalikes produces. It still does not clarify edge cases such as supplying both text and path.

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?

States a specific verb and resource: check a post against platform limits, with exact platforms and counting rules. The platform-specific scope makes it clearly distinguishable from generic siblings such as text_word_count.

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?

Explains the use case (checking post text against platform limits) and how to supply input via text or path, with an optional platform filter. It does not name alternatives or say when not to use it, so it falls short of a 5.

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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