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facebook_post_comments

Get comments on a Facebook post by post ID. Returns each comment's text, author details, reaction and reply counts, date, and any attachment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pagesNoNumber of pages to fetch (1-10, default 1). Billed per page.
post_idYesFacebook post ID (pfbid or numeric, from facebook_page_posts, facebook_group_posts, or search_facebook_posts)
get_sentimentNoAdd AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small per-page surcharge.

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description bears the burden. It discloses that the tool is a read operation ('Get') and describes the return structure, but does not mention auth requirements, rate limits, pagination behavior, or potential errors. It provides some behavioral context but lacks depth expected for an unannotated tool.

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 a single, concise sentence that front-loads the action and resource. Every word adds value, with no repetition of schema details or extraneous information. It is appropriately sized for the tool's complexity.

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?

With no output schema and no annotations, the description adequately covers the return values, which is the main missing piece. It lacks some details like pagination or error behavior, but those are partially covered by the schema (pages parameter). The description is sufficient for an agent to select and invoke the tool correctly in most cases.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description adds no additional meaning about parameters beyond what the schema already states (e.g., post_id, pages, get_sentiment). It does not explain parameter nuances or relationships, so it 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 states a specific verb ('Get') and resource ('comments on a Facebook post'), and differentiates from sibling comment tools by specifying 'Facebook post' and 'by post ID'. It also lists the exact return fields (text, author details, reaction/reply counts, date, attachment), making the purpose unambiguous.

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 implies usage (when you have a Facebook post ID and want comments) but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusions or alternative tools are mentioned, so guidance is only implicit from the name and context.

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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TDQS

B3.1/5.0
Disambiguation4/5

Most tools are clearly scoped by platform and resource (e.g. search_twitter vs twitter_user_tweets vs twitter_tweet_details). A few pairs like twitter_tweet_comments vs twitter_user_replies or facebook_page_posts vs search_facebook_posts could cause minor confusion, but descriptions generally clarify the distinction.

Naming Consistency4/5

The dominant pattern is snake_case with a platform_prefix_resource suffix, and search_* consistently marks search operations. Minor deviations include noun-style names like amazon_best_sellers and place_photos, and the odd get_ skill/comments tools, but the overall convention is predictable.

Tool Count2/5

74 tools is far beyond the typical well-scoped MCP server, even for a multi-platform API aggregator. The breadth is justified by the many platforms covered, but an agent will face a very large action space, and this could reasonably be split into per-platform servers.

Completeness4/5

The server provides strong lifecycle coverage for its read-only domain: search, profile/details, posts, and engagement data across most platforms. Gaps exist for some platforms (e.g. no LinkedIn person profile, no Facebook event details, no Truth Social profile/search, no Reddit subreddit-specific tools), but the core workflows are well covered.

Resources