IA-QA — 152 QA & Dev Tools for AI Agents

function_call_validate

Read-onlyIdempotent

Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema (types, enums, ranges), no extra/missing args. Reads the definitions as OpenAI (parameters), MCP (inputSchema) or Anthropic (input_schema) write them, or as plain required_args/optional_args; reads the call as arguments (object or JSON string), input (Anthropic tool_use) or an OpenAI {function:{…}} wrapper.

Input Schema

Name	Required	Description	Default
`function_call`	Yes	The function call object from LLM (e.g. { "name": "get_weather", "arguments": {"city":"Paris"} })
`allowed_functions`	Yes	List of allowed function definitions, in OpenAI, MCP or Anthropic form, or as {name, required_args, optional_args}

Output Schema

Name	Required	Description	Default
`valid`	No
`errors`	No
`error_count`	No
`function_name`	No
`provided_args`	No
`required_args`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 31, 2026

addedInput schema / examples

Added value: +[
+  {
+    "allowed_functions": [
+      {
+        "name": "get_weather",
+        "required_args": [
+          "city"
+        ]
+      }
+    ],
+    "function_call": {
+      "arguments": {
+        "city": "Paris"
+      },
+      "name": "get_weather"
+    }
+  }
+]

Changed4 schema fields changedOct 27, 2026

changedInput schema / properties / allowed_functions / description

Previous value: -"List of allowed function definitions"New value: +"List of allowed function definitions, in OpenAI, MCP or Anthropic form, or as {name, required_args, optional_args}"

addedInput schema / properties / allowed_functions / items / properties / inputSchema

Added value: +{
+  "additionalProperties": true,
+  "description": "MCP form",
+  "type": "object"
+}

addedInput schema / properties / allowed_functions / items / properties / input_schema

Added value: +{
+  "additionalProperties": true,
+  "description": "Anthropic form",
+  "type": "object"
+}

addedInput schema / properties / allowed_functions / items / properties / parameters

Added value: +{
+  "additionalProperties": true,
+  "description": "OpenAI form: a JSON Schema of the arguments",
+  "type": "object"
+}

Changed1 schema field changedJun 21, 2026

addedOutput schema / properties

Added value: +{
+  "error_count": {
+    "type": "number"
+  },
+  "errors": {
+    "type": "array"
+  },
+  "function_name": {},
+  "provided_args": {},
+  "required_args": {},
+  "valid": {
+    "type": "boolean"
+  }
+}

Changed1 schema field changedJun 18, 2026
- addedInput schema / properties / function_call / additionalProperties
```
Added value: +true
```

Changed1 schema field changedJun 10, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}

First observedMay 26, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds useful behavioral context by enumerating the accepted input dialects (OpenAI `parameters`, MCP `inputSchema`, Anthropic `input_schema`, plain required/optional args) and call shapes, though it says nothing about failure reporting (which the output schema covers).

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?

Purpose is front-loaded in the first clause, and every subsequent clause adds distinct information about accepted formats. It is a single dense run-on sentence, which hurts scannability slightly but wastes no content.

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 an output schema present, both parameters fully described, and the accepted definition/call dialects enumerated, an agent has everything needed to invoke this correctly. Nothing material is missing.

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 100%, so the baseline is 3. The description goes beyond the schema by spelling out the alternative shapes each definition and call object may take, which materially helps an agent construct a valid `allowed_functions` entry or unwrap a `{function:{…}}` call.

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 (validate) and resource (LLM function call / tool_use output), then enumerates exactly what is checked: name in allowed list, argument types/enums/ranges, extra/missing args. This clearly separates it from siblings like validate_mcp_response and validate_agent_trajectory.

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 makes the scope explicit — validating function-call output against definitions — which implies when to reach for it versus response/trajectory validators. It does not, however, name those sibling tools or state exclusions, so it stops short of a full 5.

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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