列热门主理人
list_creators【何时用】用户想看「有哪些做一人公司的人」「最热门的主理人」时。返回主理人卡片:昵称 / 头像 / 简介 / 作品数 / isStub(是否爬虫导入占位号,false=已认领真人)。
【后续 drill-down】可以接 get_creator 看某位主理人的完整作品列表。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 返回条数,默认 12 |
list_creators【何时用】用户想看「有哪些做一人公司的人」「最热门的主理人」时。返回主理人卡片:昵称 / 头像 / 简介 / 作品数 / isStub(是否爬虫导入占位号,false=已认领真人)。
【后续 drill-down】可以接 get_creator 看某位主理人的完整作品列表。
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 返回条数,默认 12 |
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?
Annotations already cover readOnlyHint, idempotentHint, and destructiveHint, so the description doesn't need to restate safety. It adds valuable behavioral context by defining the isStub field's semantics (crawler placeholder vs. claimed real person) and listing the exact fields returned in each creator card. A small gap is that it doesn't mention ordering by popularity, despite 'hot' in the title.
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, with clear sections for 'when to use' and 'follow-up drill-down'. Every sentence adds value: trigger condition, return card fields, the important isStub clarification, and the downstream get_creator suggestion. No filler or redundant restatement of the name/title.
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 simple read-only list tool with one optional parameter and no output schema, the description covers the essential context: when to invoke it, what the response contains, and what to do next (get_creator). It is slightly incomplete in not specifying result ordering or pagination behavior beyond the schema's limit default, but these are minor given the tool's simplicity.
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 fully documents the only parameter 'limit' with type, range, and default value, so schema coverage is 100%. The description adds no extra meaning for the limit parameter, which is acceptable; baseline 3 is appropriate when the schema handles parameter semantics completely.
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 clearly states the tool's action: returning a list of hot creators ('returns creator cards') with a specific trigger condition ('when users want to see who runs one-person companies'). The scope is differentiated from general people search by the 'hot creator' and 'one-person company' framing, but it does not explicitly contrast with siblings like list_talent, so it misses full distinction.
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 provides an explicit 'when to use' condition: when users want to discover well-known solo-company creators. It also suggests a follow-up action (get_creator) for drill-down, which helps agent routing. However, it does not mention when NOT to use this tool or name alternative listing/search tools such as search_people or list_talent, so a fully explicit comparison is absent.
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.
Each tool has a clearly documented purpose, often with explicit 'when to use' guidance and cross-references, making the vast majority easy to tell apart. A few clusters (get_my_brief, get_my_positioning, get_my_work, get_my_dispatch) and data-overlapping get_my_card vs get_my_profile require careful reading, but descriptions are detailed enough to prevent serious misselection.
The overwhelming majority follow snake_case verb_noun conventions (create_product, update_need, list_my_signups). Minor deviations include noun-only feed names (personalized_feed, random_feed), inconsistency between 'prefs' and 'preferences' in notification tools, and a mix of update_* and set_* for mutations, but the pattern remains predictable overall.
137 tools is an extreme mismatch for any MCP server, far exceeding the 50+ threshold for a score of 1. Even with a broad multi-domain platform, this volume makes tool selection and navigation impractical and heavily burdens the agent's context window.
The surface covers full lifecycles for needs, products, activities/signups, conversations, collaboration goals/tasks, dispatch, profile/onboarding, and supporting resources like companies, parks, policies, and ratings. Deliberate omissions (no user-post creation, no organizer profile editing via agent) are explicitly documented, so core workflows have no obvious dead ends.