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Get Threads Conversation

threads_get_conversation
Read-only

Retrieve the complete conversation tree for a Threads post, including all nested replies. Optionally reverse chronological order.

Instructions

Gets the full conversation tree (all replies at all levels) for a Threads post.

Args:

  • media_id (string): Root thread media ID

  • reverse (boolean, optional): Reverse chronological order

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reverseNo
media_idYes
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown
Behavior3/5

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

Annotations already declare the operation as a safe read (readOnlyHint=true, destructiveHint=false, idempotentHint=false), and the description adds the key behavioral trait that the tool recursively returns the entire reply tree at all nesting levels rather than a single page. However, it does not disclose pagination limits, rate-limit exposure, or what shape the returned tree takes beyond the bare notion of a tree. With annotations covering most of the safety profile, a 3 is fair.

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?

The one-line purpose statement is dense and front-loaded with the key scoping phrase ('all replies at all levels'). The Args block repeats schema information without adding value for media_id and reverse, which is slight waste, but overall the text is compact and the central purpose is stated immediately.

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

Completeness3/5

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

For a read-only, single-parameter-required tool, the description mostly suffices: it explains what is returned and the ordering option. However, it does not pass a structured output schema and does not clarify the response_format parameter, nor does it distinguish itself from the near-identical sibling threads_get_replies. An agent can call the tool correctly, but may not pick it over the sibling for the right scenario.

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

Parameters2/5

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

The Args block documents media_id and reverse, but the parameter_semantics coverage signal shows schema description coverage is only 33%, and the third parameter response_format is absent from the description entirely. Since response_format is a functional enum ('markdown' vs 'json') that changes the output shape, the omission leaves a real gap that the schema alone cannot fill because its schema description is minimal.

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 states a specific verb ('Gets') and a precise resource ('full conversation tree... for a Threads post') and explicitly scopes it as 'all replies at all levels.' This distinguishes it from the sibling threads_get_replies, which likely returns a flatter or single-level structure.

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 guidance is given on when to use this tool versus the sibling threads_get_replies, which has a highly similar purpose. The 'full conversation tree' phrasing implies it is the comprehensive variant, but no explicit when-to-use or when-not-to-use instruction is provided, so an agent must infer the selection criteria.

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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