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anythink-cloud

Anythink-MCP

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Register a new Anythink platform account with email and password when the user has none; confirm the email, then log in to continue.

Instructions

Register a brand-new Anythink platform account with email and password. Use this only when the user has no account yet; if they already have one, use 'login' instead. This creates the top-level user identity — it does not create a billing account or project (do that with 'accounts_create' and 'projects_create' after logging in). On success the user may need to click an email confirmation link before 'login' works; tell them to confirm, then call 'login'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesEmail address — becomes the login identifier and must be unique
lastNameYesUser's last name
passwordYesPassword for the new account; choose a strong value
firstNameYesUser's first name
referralCodeNoOptional referral code, if the user was invited by another customer

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.2.27
    • removedInput schema / description
      Removed value: -"Register a brand-new Anythink platform account with email and password. Use this only when the user has no account yet; if they already have one, use 'login' instead. This creates the top-level user identity — it does not create a billing account or project (do that with 'accounts_create' and 'projects_create' after logging in). On success the user may need to click an email confirmation link before 'login' works; tell them to confirm, then call 'login'."
    • changedInput schema / properties / referralCode / type
      Previous value: -"string"New value: +[
      +  "string",
      +  "null"
      +]
    • removedInput schema / title
      Removed value: -"signup"
  2. Changed6 schema fields changedv0.2.21
    • changedInput schema / description
      Previous value: -"Create a new Anythink account"New value: +"Register a brand-new Anythink platform account with email and password. Use this only when the user has no account yet; if they already have one, use 'login' instead. This creates the top-level user identity — it does not create a billing account or project (do that with 'accounts_create' and 'projects_create' after logging in). On success the user may need to click an email confirmation link before 'login' works; tell them to confirm, then call 'login'."
    • changedInput schema / properties / email / description
      Previous value: -"Email address"New value: +"Email address — becomes the login identifier and must be unique"
    • changedInput schema / properties / firstName / description
      Previous value: -"First name"New value: +"User's first name"
    • changedInput schema / properties / lastName / description
      Previous value: -"Last name"New value: +"User's last name"
    • changedInput schema / properties / password / description
      Previous value: -"Password"New value: +"Password for the new account; choose a strong value"
    • changedInput schema / properties / referralCode / description
      Previous value: -"Referral code (optional)"New value: +"Optional referral code, if the user was invited by another customer"
  3. First observedv0.2.6

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful behavior: it creates only the top-level user identity (not billing/project), and success may require email confirmation before 'login' works. It omits auth requirements (public vs authenticated), error cases, and any rate limits, leaving minor gaps.

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

Conciseness5/5

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

Four tightly scoped sentences, front-loaded with purpose then routing then scope then follow-up. Every sentence earns its place with no redundancy.

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?

No output schema and no annotations, so the description must stand alone; it covers purpose, alternatives, scope of creation, and the post-signup confirmation flow that affects the next call. Nothing an agent needs to invoke it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all five parameters (including referralCode optionality and email uniqueness). The description adds no parameter-level detail beyond the schema, making the baseline 3 correct.

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?

States a specific verb and resource ('Register a brand-new Anythink platform account with email and password') and explicitly distinguishes itself from the sibling 'login' by describing the no-account-yet condition. An agent can tell it apart from login without opening either schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('only when the user has no account yet') and when-not ('if they already have one, use login'), plus negative scoping that routes to 'accounts_create' and 'projects_create' for downstream objects. Nothing about selection is left to inference.

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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