CCDV-F Tools and MCP Integration Practice Question
A developer is defining a tool for Claude that fetches the current stock price for a ticker symbol. The tool will be called `get_stock_price`. Which `input_schema` definition best enables Claude to call the tool correctly?
⚠ Common exam trap
The trap here is thinking a descriptive string schema or a permissive object is enough, when Claude needs named typed properties and an explicit required list.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A JSON Schema object with `type: "object"`, a `properties` entry for `ticker` of `type: "string"`, and a `required` array containing `"ticker"`.
Tool definitions should describe inputs as a JSON Schema object with named, typed properties and a `required` array. For a stock lookup, a required string `ticker` tells Claude exactly what to supply. Permissive schemas, non-object root types, or missing required/description fields all degrade the model's ability to produce correct tool call arguments, so the fully specified object schema is the right choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A JSON Schema object with `type: "object"` and a free-form `additionalProperties: true` allowing any fields.
Why it's wrong here
Allowing arbitrary properties gives Claude no guidance about the expected parameter name or type. The model might invent fields like `symbol`, `company`, or `price_type`, producing inputs the handler cannot process. A permissive schema also weakens the model's decision about when the tool applies. Explicit named properties with types are what make tool calling reliable for a simple ticker lookup.
- ✗
A JSON Schema object with `type: "object"` and a `properties` entry for `ticker`, but no `required` array and no `description`.
Why it's wrong here
Omitting `required` and `description` leaves Claude uncertain whether `ticker` is mandatory and what it should contain. The model may call the tool with an empty or missing `ticker`, or fail to call it when the user clearly names a stock. Clear parameter descriptions and a `required` declaration materially improve call accuracy, especially for short prompts where context is thin.
- ✓
A JSON Schema object with `type: "object"`, a `properties` entry for `ticker` of `type: "string"`, and a `required` array containing `"ticker"`.
Why this is correct
Claude relies on the tool's JSON Schema to decide when and how to call it. Declaring an object with a typed `ticker` property and marking it required gives the model the parameter name, type, and obligation it needs to emit a valid `tool_use` input such as `{"ticker": "AAPL"}`. This is the standard, fully specified shape expected by the Messages API.
- ✗
A JSON Schema object with `type: "string"` and a `description` explaining that the value should be a ticker symbol.
Why it's wrong here
The top-level tool input must be an object so that named parameters can be passed. A bare string schema would force Claude to guess a property name or send a raw string, which does not match the expected `tool_use` input structure. The description alone cannot substitute for a typed `properties` map, so this definition would lead to malformed or rejected inputs.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
This CCDV-F practice question is part of Courseiva's free Anthropic certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CCDV-F exam.