Skip to main content
Glama

API Direct

by io.apidirect

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 (1-20, 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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / pages / description
      Previous value: -"Number of pages to fetch (1-10, default 1). Billed per page."New value: +"Number of pages to fetch (1-20, default 1). Billed per page."
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

The description clearly signals a read-only operation via 'Get' and describes the returned data, which is helpful given no annotations are present. However, it does not mention pagination behavior, per-page billing, or the sentiment analysis surcharge; these are documented only in the input schema, and the description itself carries the burden in the absence of 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 two sentences, front-loads the core purpose, and provides a compact list of returned fields with no filler or redundancy. Every sentence earns its place.

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 fetch tool, the description gives enough context about what the tool returns and what input is needed, while the schema covers detailed parameter semantics. There is no output schema, but the return-field summary is sufficient for an agent to understand the result shape; missing guidance on when to use the tool is accounted for in the usage_guidelines score.

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?

Schema description coverage is 100%, so the input schema already explains all parameters thoroughly. The tool description adds little parameter-specific meaning beyond referring to 'post ID' and the return shape, which does not materially improve parameter understanding.

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 uses a specific verb and resource: 'Get comments on a Facebook post by post ID.' It clearly indicates the platform, the entity, and the identifying input, and the summary of returned fields reinforces the tool's function. The platform and object make it easy to distinguish from siblings like get_youtube_comments or instagram_post_comments.

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?

Usage context is only implied: the tool is clearly for Facebook post comments, and the post_id param description in the schema hints that IDs come from other Facebook post tools. However, the tool description itself gives no explicit guidance about when to prefer this tool over alternatives, and there is no mention of when not to use it.

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

All API Direct toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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