Aident AI

capabilities_preflight

Read-only

Validate an Action input, estimate its Aident credit cost, and return any per-Action approval requirement without executing it. Use this before dynamically priced or metered Actions when the cost depends on input.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	Exact canonical Action name supplied directly or returned by capabilities_search or capabilities_get, for example "composio:reddit_tools:reddit_get_unread_inbox".
`input`	No	Input arguments to validate and price.
`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.

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"
+}

changedInput schema / required

Previous value: -[
-  "name"
-]New value: +[
+  "name",
+  "context",
+  "llm_model"
+]

Changed1 schema field changedSep 9, 2026

changedInput schema / properties / name / description

Previous value: -"Exact canonical Action name returned by capabilities_search or capabilities_get, for example \"composio:reddit_tools:reddit_get_unread_inbox\"."New value: +"Exact canonical Action name supplied directly or returned by capabilities_search or capabilities_get, for example \"composio:reddit_tools:reddit_get_unread_inbox\"."

First observedAug 7, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds the preflight nature ('without executing it') and explicitly lists what it returns (validation, cost estimate, approval requirement), which goes beyond the annotation and clarifies the tool's non-mutating behavior. 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.

Conciseness5/5

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

Two sentences with zero redundancy. The first sentence front-loads the core purpose and outcome; the second provides the usage condition. Every word earns its place.

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

Completeness4/5

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

For a tool with 4 parameters and no output schema, the description covers the primary inputs, the purpose, and the key outputs (validation, cost, approval). It does not detail the exact return structure, but the explicit mention of the three outputs gives sufficient guidance for an agent to call it and interpret results.

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% with rich descriptions for each parameter (e.g., name example, context privacy rules, llm_model instructions). The tool description adds no parameter-specific information, so it stays at the baseline for high coverage.

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 uses a specific verb ('Validate', 'estimate', 'return') with a clear resource ('an Action input') and outcome ('without executing it'). It explicitly contrasts with capabilities_execute by stating it does not execute, effectively distinguishing it from the sibling tool.

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 provides clear context: 'Use this before dynamically priced or metered Actions when the cost depends on input.' This tells the agent when to invoke it. It does not explicitly name alternatives or state when not to use it, but the condition is specific enough to route correctly.

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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