claim_creator_by_token【需要登录】用邮件令牌把某个（爬虫预录的）占位创客号名下的全部产品 + 会话一次性转到当前账号。令牌来自冷启动外联邮件里的链接（/u/{creatorId}?ct={token}）。
【注意】不可逆。与 claim_product（单个产品认领码）不同——这是整号认领。失败返回 ok=false + reason（invalid_token / not_found / already_claimed / self）。
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes | 认领令牌（邮件链接 ?ct=<token>） | |
| creatorId | Yes | 占位创客号的用户 id（邮件链接 /u/<creatorId>） |
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?
The description discloses critical behavioral traits beyond the annotations: the operation is irreversible, it transfers all products and conversations, and failure returns ok=false with specific reasons such as invalid_token, not_found, already_claimed, or self. The irreversibility warning adds meaningful context without contradicting destructiveHint=false, since the operation transfers rather than destroys data.
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: a lead sentence states the action, a second sentence explains token provenance, and a note block covers irreversibility, sibling differentiation, and failure modes. Every sentence adds information with no filler.
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?
Despite having no output schema, the description covers preconditions, input provenance, effect scope, irreversibility, alternative tools, and failure semantics. This is complete enough for an agent to invoke the tool correctly and interpret likely failure responses.
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 already covers both parameters at 100%, and the description adds valuable provenance details: token maps to ?ct=<token> in the email link, while creatorId maps to /u/<creatorId> and refers to a pre-recorded placeholder creator account. This helps the agent correctly populate the parameters.
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?
States a specific verb and resource: using an email token to transfer all products and conversations from a placeholder creator account to the current account. It explicitly distinguishes itself from claim_product, so an agent can tell this is a whole-account claim rather than a single-product claim.
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?
Provides clear usage context: login is required, the token comes from a cold-start outreach email link, and the intended alternative is claim_product for single-product claim codes. This explicitly routes the agent to the right tool for the right scope.
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.