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x-use

x-use

by ihuzaifashoukat
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

reply_to_tweet

Draft or publish replies to X/Twitter posts with explicit or auto-generated text, optional media, and approval before posting.

Instructions

Reply to a tweet. Pass explicit text, or "auto" to generate the reply with the LLM from the tweet's actual content (a read-only fetch is performed so the draft shows the real reply text). In draft mode (default) nothing is posted until approve_draft(draft_id). media accepts local attachments on the async browser backend. For conversation-wide context, read get_thread before supplying explicit reply text.

Input Schema

NameRequiredDescriptionDefault
textNoauto
mediaNo
accountYes
tweet_urlYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.2.0
    • addedInput schema / properties / media
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Media"
+}
  2. Addedv0.1.1

TDQS

A3.9/5.0
Behavior4/5

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

Annotations only give the generic safety profile (write, non-idempotent, open-world); the description adds valuable behavior: nothing posts until approve_draft, auto performs a read-only fetch, and media only works on the async browser backend. That is genuine context beyond the structured hints.

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?

Front-loaded with the core action, then layered constraints and pointers. Five dense sentences with little waste, though the media/backend note and get_thread advice could be trimmed slightly.

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?

An output schema exists so return values needn't be described, and the description covers the draft/approve lifecycle, auto-generation behavior, and media constraints. The one gap is what `account` selects, which matters for a multi-account posting tool.

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?

With 0% schema description coverage the description must carry the load, and it explains `text` (explicit vs "auto") and `media` (local attachments on async backend) well. However `account` and `tweet_url` are entirely unexplained, leaving half the parameters undocumented.

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?

States a specific verb+resource ('Reply to a tweet') that an agent can map directly to the action. It clarifies scope (draft-by-default, approval-gated) but never contrasts itself with close siblings like prepare_reply, post_tweet, or queue_post, so differentiation is only implicit.

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 real branching guidance: pass explicit `text` vs "auto" for LLM generation, notes draft mode is the default and requires approve_draft(draft_id), and points to get_thread for conversation-wide context. It stops short of stating when to prefer this over prepare_reply/queue_post.

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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