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atmosphere-ai

AdvisorPPC X Organic

Unlike a post

x_organic_unlike

Removes a like from a specified post on X/Twitter. Undo accidental likes or manage engagement while keeping community interactions policy-safe.

Instructions

DELETE /2/users/:id/likes/:tweet_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
post_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

The readOnlyHint=false annotation already indicates mutation, and the DELETE method in the description adds more specificity by naming the resource being affected. However, the description does not disclose auth requirements, idempotency, side effects of unliking when no like exists, or how openWorldHint=true behaves. It adds only a thin layer of behavioral detail beyond the annotations, so the description carries an incomplete but non-contradictory disclosure.

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 description is extremely short and contains no filler, making it easy to scan. However, it is more of an API endpoint signature than a prose explanation, so its brevity is structural rather than informative. Every word earns its place, though the description could have used the space to clarify the ambiguous parameter.

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

Completeness2/5

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

This is a simple one-parameter tool with no output schema, so complex return value documentation is not required. Yet the description omits any usage context and leaves the post_id semantics unexplained against a 0% schema coverage. An agent would struggle to know whether to pass a post ID or a tweet ID and whether the endpoint path should be interpolated differently, making the definition incomplete for a tool that should be very simple.

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?

Schema description coverage is 0%, so the description carries the full burden of explaining the post_id parameter, but it only provides endpoint placeholders like :tweet_id. The schema requires post_id, while the description references tweet_id separately, leaving the mapping ambiguous. There is no explanation of what post_id represents, how it maps to the URL path, or whether the user id is also required.

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 names a concrete HTTP verb and resource: DELETE on /2/users/:id/likes/:tweet_id, which clearly identifies the action of removing a like. It is also clearly distinct from sibling x_organic_like because one tool adds a like and the DELETE verb is explicit here. No agent could confuse this tool with a post deletion or repost action after reading the endpoint.

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?

The description gives no guidance about when to use that tool versus alternatives. It does not mention whether this should be used for unliking a post, unbookmarking, deleting a post, or any prerequisite states such as requiring an existing like. Usage context is entirely left to inference.

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