Threads MCP

Read the reply approval queue

get_pending_replies

Read-onlyIdempotent

Retrieve replies pending approval on posts with reply approvals enabled, allowing you to moderate them before public visibility.

Instructions

Replies waiting for approval on posts published with enable_reply_approvals. They are invisible to everyone until approved. Empty unless reply approvals were switched on for the post.

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": "get_pending_replies"
  }
}

Input Schema

Name	Required	Description
`limit`	No	How many to return, 1-100.
`cursor`	No	Continue from a previous page. Pass the `cursor` attribute from the last result.
`account`	No	Which connected Threads profile to act as, by username (for example 'thenavidm'). Defaults to the first connected profile. Call list_accounts to see them.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already mark this as read-only and idempotent, and the description adds value beyond that by explaining that pending replies are invisible to everyone until approved and that the result set is empty unless reply approvals were enabled. This is useful behavioral context not present in 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three short sentences with no filler. It front-loads the core meaning with the first sentence and uses the remaining two to clarify important behavioral details.

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 read-only list operation, the description plus well-documented parameters and safe annotations are mostly sufficient. There is no output schema, but the description clarifies what is being returned, so an agent can understand the tool's purpose without needing further detail.

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?

The input schema describes all three parameters with complete coverage, so the schema carries the parameter-documentation burden. The description adds no additional parameter-specific meaning, which is acceptable but does not exceed the baseline.

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 resource: replies waiting for approval on posts with enable_reply_approvals. It also distinguishes itself from sibling tools like get_replies and get_all_replies by noting that pending replies are invisible until approved.

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 gives clear context for when the tool is relevant: posts published with reply approvals enabled, and notes the queue is empty unless that setting was used. It does not explicitly name alternative tools or state when not to use it, but the context is strong enough for an agent to infer appropriate usage.

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