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Livia-Zaharia

Just Facebook MCP Server

get_post_top_commenters

Identify the most active commenters on a Facebook post by analyzing user engagement and comment frequency.

Instructions

Get the top commenters on a post. Input: post_id (str) Output: list of user IDs with comment counts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
post_idYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output format ('list of user IDs with comment counts'), which is helpful, but lacks details on permissions, rate limits, pagination, or error handling. For a read operation with no annotation coverage, this is a significant gap in transparency.

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 extremely concise and front-loaded, with three short sentences that efficiently convey purpose, input, and output. There's no wasted text, and every sentence adds value, making it easy for an agent to parse quickly.

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?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output but lacks details on behavioral traits and usage context. Without annotations or output schema, it should do more to explain limitations or expected behavior, but it meets the baseline for a simple read tool.

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

Parameters4/5

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

The description adds meaning beyond the input schema by specifying that 'post_id' is a string input and clarifying the tool's focus on 'top commenters.' With 0% schema description coverage and only 1 parameter, this compensates well, though it doesn't explain what 'top' means (e.g., by count or other criteria).

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?

The description clearly states the tool's purpose: 'Get the top commenters on a post.' It specifies the verb ('Get') and resource ('top commenters on a post'), making it easy to understand. However, it doesn't explicitly differentiate from siblings like 'get_post_comments' or 'get_post_engaged_users', which might provide overlapping or related data, so it misses the highest score.

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 provides no guidance on when to use this tool versus alternatives. With many sibling tools related to posts and comments (e.g., 'get_post_comments', 'get_post_engaged_users'), there's no indication of context, exclusions, or comparisons. This leaves the agent to guess based on tool names alone.

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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