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anythink-cloud

Anythink-MCP

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Pay: offers user code

pay_offers_user_code

Look up a user's personal referral code by ID, returning its slug, offer name, and redemption count; creates a code if none exists.

Instructions

Look up a user's personal referral code. Returns one user's personal referral code (by numeric user id) with its slug, offer name and redemption count. If the user has no code yet the server generates one, so this call can create a code. To list all codes on one offer, use pay_offers_codes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_idYesNumeric user id. Find it with users_list.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.37

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare readOnlyHint=false, and the description explains exactly why: if no code exists the server generates one, so the call can create a code. It also discloses the returned fields (slug, offer name, redemption count), which the absent output schema would otherwise leave unknown. This is meaningful disclosure beyond the annotations rather than a restatement of them.

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 tight sentences, each earning its place: purpose, return shape, side effect, and alternative. The purpose and the create-on-demand caveat are front-loaded where the agent will see them.

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 supplies the return fields and the write side effect, and it routes to the correct alternative. An agent has everything needed to call this correctly and interpret the result.

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% and the single parameter is documented there (including how to find it with users_list). The description only restates 'by numeric user id', adding no syntax or format detail beyond the schema, so the baseline 3 applies.

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+resource (look up a user's personal referral code) with explicit scope (one user, by numeric user id). It also names the sibling it is not (pay_offers_codes), so an agent can distinguish the two 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 the exact selection rule and names the alternative: use this to fetch one user's code, use `pay_offers_codes` to list all codes on an offer. No inference required to route correctly among the many pay_* siblings.

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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