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twitr.sh

by sh.twitr

x_compose

Draft tweets with AI: generate variants from a topic and goal, refine an existing draft, or score a draft for likely engagement — with tone, call-to-action, and style-matching options. Flat sub-cent price per step.

Guidance: step=generate (topic + goal required; optional tone/styleUsername/additionalContext/callToAction/mediaType/hasLink/hasMedia), refine (draft required), or score (draft required). Returns text variants with scores and suggestions. Flat floor price per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNo
stepYesComposer step.
toneNo
draftNo
topicNo
hasLinkNo
hasMediaNo
mediaTypeNo
callToActionNo
styleUsernameNo
additionalContextNo

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description must carry full weight. It discloses that the tool returns text variants with scores and suggestions, and mentions the flat price. It does not mention side effects like account interaction or draft persistence, which would be beneficial but not critical for a composition tool.

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?

The description is two sentences plus a concise guidance block. It front-loads the action, groups related information, and avoids redundancy. Every sentence adds value, from the overview to the step-specific requirements.

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?

Given 11 parameters, no output schema, and no annotations, the description covers the tool's functionality well. It explains the three modes, required vs optional parameters, and return values. Minor gaps: it doesn't clarify whether drafts are saved or if the tool interacts with the account, but the core use is fully specified.

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

Parameters5/5

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

The input schema has only 9% description coverage (just 'step' has a short note). The description compensates fully by listing all parameters grouped by step (topic, goal, tone, etc.) and explaining their roles. It adds meaning beyond the schema, making the parameter intent clear despite minimal schema descriptions.

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 clearly identifies the tool as a Twitter compose assistant with three distinct steps (generate, refine, score), each with specific inputs. It differentiates from siblings like x_write and x_read by focusing on draft creation, refinement, and scoring rather than posting or reading tweets.

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?

The description provides explicit guidance on when to use each step and which parameters are required or optional. It mentions pricing but does not explicitly state when not to use the tool or contrast with alternatives. However, the step-based instructions are clear enough for an agent to pick the correct mode.

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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TDQS

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (write, read, search, monitor, compose, etc.), but some overlaps exist: x_read includes trending topics while x_radar is dedicated to trends, and x_timeline provides engagement lists that overlap with x_extract's extractors. Descriptions help differentiate them, but an agent might initially confuse the boundary between x_read, x_search, and x_timeline for tweet retrieval.

Naming Consistency4/5

All tool names share the x_ prefix and are single words, but mix verb forms (read, search, write, compose, extract, monitor) with noun forms (draws, radar, inbox, lists, profile, timeline). The convention is predictable and uniformly lowercase, but a fully consistent verb_noun or noun-only pattern would be clearer. Minor deviation: x_communities vs x_community are nearly identical and refer to reading vs managing.

Tool Count5/5

14 tools is well within the ideal 3-15 range and each tool covers a distinct functional area of X/Twitter: reading, writing, searching, monitoring, extracting, composing, community management, etc. The count feels appropriate for the broad scope of the server, neither bloated nor thin.

Completeness5/5

The tool surface covers the major X/Twitter interactions comprehensively: fully capable read (x_read, x_search, x_timeline), write (x_write, x_profile), community and list operations, real-time monitoring, bulk extraction, direct messaging, media, trends, and even AI-assisted composition and giveaways. Obvious gaps are minimal, such as no list creation/management commands, but the core lifecycle of tweets, users, communities, and accounts is well covered.

Resources