Get reviews for a Facebook page.
| Name | Required | Description | Default |
|---|---|---|---|
| pages | No | Number of pages to fetch (1-15, default 1). Billed per page. | |
| page_id | Yes | Facebook page ID (numeric) | |
| 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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / pages / descriptionPrevious value: -"Number of pages to fetch (1-10, default 1). Billed per page."New value: +"Number of pages to fetch (1-15, default 1). Billed per page."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure, and it discloses almost nothing—no mention of paginated results, per-page billing, the sentiment-analysis add-on, rate limits, or response shape. The only behavioral hints (billing, surcharge) live in the input schema's parameter descriptions, not in the tool description.
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?
A single six-word sentence that is front-loaded with the verb and fully earns its place—there is no fluff or redundancy. It is efficient and clear, though the available space could have been used to pack in pagination or billing context.
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?
With no output schema and no annotations, the description must compensate by hinting at return values and operational details, and it does neither. An agent is left to discover pagination (1-15 pages, billed per page) and the sentiment add-on only by inspecting the schema, and nothing describes what a single review record will look like.
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?
Schema description coverage is 100%, so all three parameters (page_id, pages, get_sentiment) are already documented in the schema with types, defaults, and semantic notes. Per the baseline rule, the description need not repeat param details, and it adds no param-level meaning beyond what the schema provides.
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 uses a specific verb ('get') and resource ('reviews') tied to a clear target ('a Facebook page'), so an agent understands exactly what data this tool returns. However, it does not differentiate itself from sibling tools that also return review-like data (e.g., place_reviews, amazon_seller_reviews, facebook_post_comments), relying entirely on the name to disambiguate.
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?
No guidance is provided about when to select this tool over alternatives. With a large sibling set containing other review- and comment-oriented fetchers, the description gives no exclusions, no conditions, and no routing hints beyond the bare statement of what the tool fetches.
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.