get_creator_rankingsRank social creators across the whole corpus by measured influence.
Corpus-wide, unlike get_influencers which is scoped to one asset.
Influence is computed from engagement our collectors actually observed,
so a creator we do not ingest is absent rather than ranked low — an
absence here is a coverage fact, not a judgement.| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool read-only, open-world, idempotent, and non-destructive. The description adds a valuable behavioral nuance beyond those annotations: absent creators are not ranked low but omitted because they are not ingested, so absence is a coverage fact. This is a meaningful semantic warning that helps an agent interpret results correctly.
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 compact and well structured: the core purpose is front-loaded in the first sentence, followed by a succinct sibling contrast, then an important interpretive caveat. Every sentence earns its place.
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's simplicity, the rich annotations, and the explicit ranking semantics, the description covers the most important behavioral context. Minor gaps remain: no indication of the response shape or how the optional limit parameter behaves, but these are not critical for a straightforward read-only ranking tool.
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 0%, so the description carries the burden of explaining parameters, but it never mentions 'limit' or how it affects the returned rankings. The parameter is simple and self-explanatory from its name and default, but the description still adds no meaning beyond the schema.
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 opens with 'Rank social creators across the whole corpus by measured influence,' which names a specific verb, resource, and scope. It explicitly distinguishes itself from get_influencers, which is scoped to one asset, making the tool's identity immediately clear.
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?
The description explicitly says it is corpus-wide and contrasts with get_influencers, which is scoped to one asset. This gives an agent a direct condition for choosing this tool over a key sibling.
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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