scrapecreators_facebook_ad_library
Facebook Ad Library
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Ad ID | |
| url | No | Ad URL | |
| trim | No | Set to true for a trimmed response | |
| get_transcript | No | Set to true to get transcript |
xpay✦ Marketing Collection
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
D1.4/5.0
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior, but 'Facebook Ad Library' reveals nothing about side effects, required permissions, rate limits, or response characteristics. It is a bare label, not a behavioral explanation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally short, but this is under-specification rather than effective conciseness. It contains no actionable information and fails to earn its place as a standalone description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With four parameters, no output schema, no annotations, and a large set of sibling tools, the description leaves nearly all contextual gaps unfilled. It does not explain what inputs mean in context, what the output looks like, or how this tool relates to other ad library tools.
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 baseline is 3. The description itself adds no parameter meaning beyond what the schema already provides, but the schema adequately names 'Ad ID', 'Ad URL', 'trim', and 'get_transcript' with short explanations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Facebook Ad Library' merely restates the tool name without providing a verb or action. It does not say whether the tool searches, scrapes, or retrieves ads, nor does it distinguish it from sibling tools like scrapecreators_search_ads or scrapecreators_ad_details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. Among many ad-related sibling tools, no context, prerequisites, or examples are given, leaving the agent unable to decide on appropriate usage.
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
C2/5.0
Disambiguation2/5
The tool set has significant overlap and ambiguity, particularly within the 'scrapecreators_' prefix where many tools appear to target similar social media platforms and content types (e.g., 'scrapecreators_posts', 'scrapecreators_posts_get', 'scrapecreators_post', 'scrapecreators_post_get'). Additionally, tools like 'tavily_research' and 'tavily_search' have overlapping purposes with 'web_search_exa', making it difficult for an agent to distinguish when to use each. While some tools like 'get_credits' or 'ideogram_v3' are distinct, the overall set is confusing due to redundant functionalities.
Naming Consistency2/5
Naming conventions are highly inconsistent across the tool set. There is a mix of snake_case (e.g., 'get_credits'), kebab-case (e.g., 'find-hooks'), and verbose prefixes (e.g., 'scrapecreators_'). The 'scrapecreators_' tools themselves vary in structure, with some using underscores and others not, and there are duplicate names with slight variations (e.g., 'scrapecreators_ad_details' vs. 'scrapecreators_ad_details_get'). This lack of a predictable pattern makes the tool set chaotic and hard to navigate.
Tool Count1/5
With 124 tools, the count is extremely high and inappropriate for the server's purpose, which appears to be marketing and social media data collection. This many tools suggests poor scoping, likely due to redundancy (e.g., multiple scraping tools for similar platforms) and overlapping functionalities. A well-scoped server in this domain should have far fewer tools, typically in the range of 10-30, to avoid overwhelming agents and ensure clarity.
Completeness4/5
Despite the high tool count and redundancy, the server covers a broad range of marketing-related functions comprehensively. It includes tools for social media hooks, content validation, SEO analysis (e.g., backlinks, keywords), voice archetypes, copywriting frameworks, and extensive scraping across multiple platforms. There are no obvious major gaps for the marketing domain, as it supports data gathering, content creation, and analysis across various networks and metrics, allowing agents to perform core marketing workflows effectively.