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OpenTweet MCP Server

OpenTweet MCP Server

Official
by opentweetio
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

opentweet_get_top_posts

Read-only

Retrieve your top-performing tweets ranked by engagement or other metrics. Use the data to repurpose high-impact posts or analyze what drives performance.

Instructions

Get your top-performing tweets ranked by engagement (or other metric). Returns the user's actual best-performing posts with metrics, useful for repurposing or studying what works.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "opentweet_get_top_posts"
  }
}

Input Schema

NameRequiredDescriptionDefault
daysNoRestrict to last N days (0 or omit = all time)
sortNoSort field (default: engagement)
limitNoNumber of posts to return (default 10, max 50)
x_account_idNoFilter by X account ID (multi-account users)
min_impressionsNoFilter out posts below this impression count

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0

TDQS

A4/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description adds value by explaining the return includes 'actual best-performing posts with metrics' and the purpose ('studying what works'). However, it does not detail authentication or rate limits, which are standard assumptions.

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?

Two concise sentences front-load the core function and ranking, followed by a useful purpose statement. No redundant or extraneous information.

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?

For a simple filtered read operation with 5 well-described parameters and a readOnlyHint annotation, the description conveys purpose, return type (best posts with metrics), and use case. However, it could specify the exact metrics returned (e.g., likes, retweets) since there is no output schema.

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%, so all parameters are already documented. The description adds no additional meaning beyond what the schema provides, such as that the 'sort' parameter aligns with 'ranked by engagement (or other metric)'.

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 states it retrieves 'top-performing tweets ranked by engagement (or other metric)', using specific verb 'Get' and resource 'top posts'. It differentiates from siblings like 'list_tweets' by emphasizing ranking and performance.

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

Usage Guidelines3/5

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

The description mentions the tool is 'useful for repurposing or studying what works', implying when to use it, but does not explicitly state when not to use it or list alternatives. No exclusions or comparisons to sibling tools like 'opentweet_list_tweets'.

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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