Get popular songs
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number | |
| rankType | No | Get popular or surging songs | |
| newOnBoard | No | New to top 100 | |
| timePeriod | No | Time period to get popular songs from | |
| countryCode | No | Country code to get popular songs from | |
| commercialMusic | No | Approved for business use? |
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 does not mention side effects, auth requirements, rate limits, or return shape, making it impossible for the agent to anticipate what invoking this tool will do.
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, which might seem concise, but it is under-specification. It does not convey enough information to be useful and does not earn its place as a meaningful tool guide.
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?
Given the tool has 6 optional parameters, no output schema, no annotations, and a broad name, the description is completely inadequate. It lacks any explanation of return values, platform context, or how parameters interact, making it unusable for effective tool selection.
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 covers all 6 parameters with descriptions, so schema coverage is 100%. The tool description adds no additional meaning beyond the schema, meeting the baseline but not compensating for the vague parameter names like 'timePeriod' or 'commercialMusic'.
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 'Get popular songs' clearly states a verb and resource, but it is essentially a restatement of the tool name. It fails to define what 'popular' means, which platform or context it applies to, and does not distinguish it from sibling tools like scrapecreators_get_popular_videos or scrapecreators_get_song_details.
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 on when to use this tool versus the many sibling scraping tools. There is no mention of preferred use cases, required filters, or alternative tools, leaving the agent with no directional help.
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.