Get reels from a Facebook page. Requires the reels_page_id from the page details endpoint.
| Name | Required | Description | Default |
|---|---|---|---|
| pages | No | Number of pages to fetch (1-10, default 1). Billed per page. | |
| get_sentiment | No | Add AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small per-page surcharge. | |
| reels_page_id | Yes | Reels page ID (from page details endpoint) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only mentions the ID requirement but does not disclose that this is a read-only operation, any pagination behavior (despite a 'pages' parameter), or any response format. The description is silent on side effects, authentication, or billing beyond what is already in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two short sentences that are front-loaded with the main purpose and followed by a necessary prerequisite. There is no redundant wording, and every word contributes to the meaning. It is appropriately concise for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and no annotations, so the description should explain what the response looks like or how pagination works. It does neither. While the schema explains the 'pages' parameter, the description fails to state that the tool returns a paginated list of reels, leaving the agent uncertain about the return format and usage flow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with descriptions for all three parameters. The description adds no additional parameter semantics beyond restating the requirement for reels_page_id. Since the schema fully documents the parameters, a baseline of 3 is appropriate; the description does not add value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get reels') and the resource ('from a Facebook page'), making the tool's purpose immediately understandable. It distinguishes from siblings like facebook_page_posts or facebook_page_photos by naming 'reels', though it does not explicitly differentiate from facebook_page_videos, which could overlap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage prerequisite: 'Requires the reels_page_id from the page details endpoint.' This tells the agent that it must first call the page details endpoint to obtain the ID, offering clear context on when to use this tool. However, it does not explicitly exclude alternatives or state when not to use it, so it does not reach a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
