Skip to main content
Glama

ariel_create_post

Create a Bluesky post from text up to 300 graphemes. Mentions, links, and hashtags are detected automatically, with optional language codes for localization.

Instructions

Create a new post on Bluesky with automatic mention/link/hashtag detection

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesPost text (max 300 graphemes). Mentions (@handle), links, and #hashtags are auto-detected.
langsNoLanguage codes (e.g. ['en']). Optional.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already establish readOnlyHint=false and destructiveHint=false, so the safety profile is covered. The description adds the auto-detection behavior, which is useful context, though it largely repeats what the schema's text property already states and does not disclose success/error behavior or authentication needs. No contradiction with annotations.

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?

A single sentence with no filler, front-loaded with the verb and resource, and the distinguishing feature included compactly. Every word earns its place.

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

Completeness2/5

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

For a mutation tool with no output schema, the description does not indicate what the caller gets back (e.g., post URI) or when to prefer very close siblings. Schema and annotations cover parameters and safety well, but the missing return-value and routing information leaves an agent guessing on selection and post-invocation expectations.

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% — both parameters (text, langs) are documented in the schema itself — so the baseline of 3 applies. The description's auto-detection phrase mirrors the text property description and adds no parameter-level information beyond the schema.

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?

The description states a specific verb-resource pair ('Create a new post on Bluesky') and adds a distinguishing behavioral feature (automatic mention/link/hashtag detection). It is clear and actionable, but it does not explicitly distinguish itself from close siblings like ariel_create_post_with_link_card or ariel_create_post_with_images, stopping just short of full sibling differentiation.

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

Usage Guidelines2/5

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

No when-to-use guidance, exclusions, or alternatives are mentioned. The sibling set contains several near-equivalents (ariel_create_post_with_link_card, ariel_create_post_with_images, ariel_create_thread, ariel_reply), and the description gives the agent no basis for choosing among them; the auto-detection phrase describes behavior, not usage direction.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/TickTockBent/ariel'

If you have feedback or need assistance with the MCP directory API, please join our Discord server