LinkedIn Ad Library
| Name | Required | Description | Default |
|---|---|---|---|
| company | No | Company name | |
| endDate | No | End date | |
| keyword | No | Keyword to search | |
| countries | No | Countries filter | |
| startDate | No | Start date | |
| paginationToken | No | Pagination token |
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It merely names the resource and provides no information about what the tool returns, whether it performs a read or search operation, pagination behavior, authentication needs, or any other behavioral trait.
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 is extremely short, but this is under-specification rather than conciseness. It conveys no useful information and fails to earn its place as a tool description.
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?
For a tool with 6 optional parameters, no output schema, and no annotations, the description is completely inadequate. It does not explain what kind of ad library results are returned, how filters interact, or what paginationToken is for, leaving the agent unable to invoke the tool effectively.
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% description coverage, so the baseline is 3. The description adds no parameter meaning beyond what the schema already provides, but it does not need to compensate significantly. However, it also does not clarify relationships between parameters (e.g., startDate/endDate) or expected formats.
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 'LinkedIn Ad Library' is essentially a tautology that restates the tool name and provides no verb or action. It does not state what the tool does (e.g., search, retrieve, or list ads) and fails to distinguish it from sibling tools like scrapecreators_search_ads or scrapecreators_company_ads.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or exclusions, leaving the agent without direction on when to select this tool over the many similarly named sibling tools.
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.
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 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.
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.
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.