API Direct

search_facebook_videos

Search Facebook videos by keyword.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`pages`	No	Number of pages to fetch (1-15, default 1). Billed per page.
`query`	Yes	Search keyword (max 500 characters)
`sort_by`	No	Sort order: most_recent or relevance (default: relevance)	relevance
`end_date`	No	Filter videos until this date
`start_date`	No	Filter videos from this date
`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.

Schema Changelog

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

Changed1 schema field changedSep 4, 2026

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 (1-15, default 1). Billed per page."

First observedAug 17, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations exist, so the description carries the full burden of behavioral disclosure, yet it discloses nothing about pagination, billing, or result limits. The pages and get_sentiment parameter descriptions mention per-page billing and the sentiment surcharge, but the tool-level description itself contributes no behavioral context for an agent evaluating cost or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single six-word sentence that is front-loaded and free of filler. It is genuinely concise, though the brevity is the flip side of the missing usage and behavioral guidance penalized elsewhere.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 6 parameters, per-page billing, an AI-sentiment surcharge, and multiple Facebook sibling tools to distinguish from, a six-word description is not complete context. With no annotations and no output schema, the agent is left unable to judge cost, result scope, or when to select this tool over facebook_page_videos.

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 schema already documents all six parameters, including the 500-character query cap, page range 1-15, sort enum, date filters, and the sentiment-analysis surcharge. The description adds no parameter-level meaning beyond echoing 'keyword,' which is already obvious from the schema, so the baseline 3 applies.

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 states a clear verb ('Search'), a specific resource ('Facebook videos'), and a method ('by keyword'), so an agent can grasp the core operation instantly. However, it does not differentiate this tool from the sibling facebook_page_videos, which also concerns Facebook videos — the scoping distinction (site-wide keyword search vs. per-page listing) is left implicit.

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?

No guidance is given on when to use this tool versus alternatives such as facebook_page_videos, search_facebook_posts, or search_facebook_events. An agent facing several Facebook search/list siblings gets no decision rule beyond the name-to-resource match, which is not enough to route correctly.

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.

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