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查我的入驻引导状态

get_onboarding_status
Read-onlyIdempotent

【需要登录】返回当前用户的入驻引导状态:completed(是否已完成)/ persona(身份)/ isFounder / creatorType(创造者类型)/ hasProduct(名下是否有产品)/ hasProfile(bio 是否已填;详细介绍 intro 是选填,不算门槛)/ hasCompany(是否建了公司)。

【何时用】帮用户「完成入驻 / 看还差哪步」时第一步先读它,再按缺口补:选身份(set_persona)→发产品(create_product)→完善资料(update_my_profile,记得写 canOffer)→可选建公司(create_company)→complete_onboarding。

【prefill——别从零开始问】返回里可能带 prefill:这个人此前在网页上报过名、或被运营在现场当面录过资料,服务端手里就有一份现成的(含 LLM 通读其报名答卷得出的 understanding 要点)。有它就当上下文用,能少打很多字

预填只减打字,不减追问:每一项都要念给用户确认,必填项一项都不能跳,complete_onboarding 的校验一条都不能绕。prefill 为 null 是常态(大多数人没有)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable behavioral context beyond this: login requirement, the prefill field's origin and meaning, the warning that prefill only reduces typing and never reduces confirmation requirements, and that prefill is null by default. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is organized into clear sections and front-loads the core purpose. It is somewhat long, and the prefill warning is repeated in two places, but the extra length is mostly justified because of the subtle and important prefill behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description fully compensates by enumerating the return fields and their exact semantics. It also covers login requirements, prefill behavior, and the downstream workflow, so an agent has everything needed to invoke and interpret this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so there are no parameter semantics to document. The baseline for 0-param tools is 4, and the description correctly avoids inventing parameter-related content.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the specific verb '返回' with a clear resource ('当前用户的入驻引导状态') and enumerates every returned field. It also differentiates itself from siblings by positioning itself as the first step in the onboarding workflow, with complete_onboarding as the final step.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use this tool: when helping users '完成入驻 / 看还差哪步', read this first. It even provides the ordered sequence of sibling tools to call based on gaps. However, it does not explicitly state when not to use it or name exclusions.

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

A4.1/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness5/5

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.

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