Aident AI

Manage Aident affiliate account

affiliate

Activate or inspect your Loadout affiliate account, request a payout, or permanently attach an affiliate code within 60 days of signup.

Input Schema

Name	Required	Description	Default
`code`	No	Affiliate code to redeem
`action`	No		status
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`termsAccepted`	No	Confirms acceptance of https://aident.ai/affiliate-program-terms version 2026-08-15

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 14, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

addedInput schema / required

Added value: +[
+  "context",
+  "llm_model"
+]

Changed2 schema fields changedAug 19, 2026

changedInput schema / properties / action / enum

Previous value: -[
-  "activate",
-  "status",
-  "redeem"
-]New value: +[
+  "activate",
+  "status",
+  "redeem",
+  "request-payout"
+]

changedInput schema / properties / termsAccepted / description

Previous value: -"Confirms acceptance of https://aident.ai/affiliate-program-terms version 2026-08-11"New value: +"Confirms acceptance of https://aident.ai/affiliate-program-terms version 2026-08-15"

AddedAug 14, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations declare non-read-only, non-destructive, closed-world, which is a low bar. The description adds genuinely useful context beyond that: the code attachment is permanent and carries a 60-day deadline. It does not, however, state payout requirements or that terms acceptance is a precondition.

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?

A single sentence that front-loads the verb set and ends with the most consequential constraint. No filler, though it packs four operations into one clause at slight cost to scanability.

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

Completeness3/5

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

With five parameters, no output schema, and a mutation-capable tool, the description omits two things an agent would want: that `termsAccepted` must be true and that `context`/`llm_model` are mandatory analytics fields. The action coverage is adequate, but the parameter story is incomplete.

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?

Schema coverage is 80%, so the schema already documents most parameters; baseline would be 3. The description earns a bump by tying the `code` parameter to a concrete constraint (must be attached within 60 days) and labeling the attach as permanent, which the schema does not convey for `action`.

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

Purpose4/5

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

The description names a specific resource (the affiliate account) and enumerates the four operations the `action` enum supports: activate, inspect/status, request payout, and redeem code. It is clear what the tool does, though it never distinguishes itself from siblings like billing or auth, which also touch account-level concerns.

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

Usage Guidelines3/5

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

Usage is mostly implied by the action enum; the description adds one real condition (code must be attached within 60 days of signup). It does not say when to prefer activate over status, nor mention prerequisites such as accepting the affiliate terms before activation.

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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