The Tool Desk
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Start by identifying where the format sits
“LLM input” can mean several different things: context placed in a prompt, a response the model must return, arguments for a tool call, or data stored and moved between application components. These boundaries have different requirements. A representation that works well for application transport may not be what a model endpoint accepts, while a readable prompt format is not necessarily a reliable machine-to-machine contract.
- Prompt context: Make instructions and data easy to distinguish, especially when the data comes from a user or another untrusted source.
- Model output: If downstream code requires specified fields and types, use a supported schema-constrained output feature rather than relying on the model to follow a formatting request.
- Tool arguments: Use the provider’s tool or function-calling interface when the model needs to invoke an application function.
- Application storage or transport: Select for typed data, language support, compactness, and schema evolution; render the content into a representation the model endpoint accepts at the model boundary.
Choose a format for the job
For model responses that must match a schema: constrained JSON output
When application code needs fields in a defined structure, use the provider’s structured-output or schema-constrained JSON feature where supported. Valid JSON and schema adherence are not the same: JSON mode can ensure syntactically valid JSON without ensuring that the response matches a particular schema. OpenAI distinguishes these options in its Structured Outputs guide. Anthropic also documents schema-constrained JSON outputs and strict tool use as distinct features that can be combined; check its current documentation for the supported behavior.
Before depending on a schema, verify that the specific model and API support the schema features your application uses, and account for refusals or other cases in which the response may not be the expected object. A schema helps define the response contract; it does not remove the need to handle unsuccessful or exceptional responses.
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For actions: use tool or function calling
Tool invocation is not just another way to ask for a JSON-shaped answer. It connects a model response to a declared function or tool and its arguments. OpenAI recommends function calling when connecting the model to tools, functions, or data, and a structured response format when the goal is to structure the model’s response. Anthropic likewise treats strict tool use separately from schema-constrained JSON output. Use the interface that matches the operation, and validate or authorize tool arguments in your application before carrying out consequential actions.
For prompt context: use readable structure and explicit boundaries
Simple context may need only plain text with clear labels. For nested or more complex content, JSON, YAML, or XML can make boundaries and relationships more apparent to both people and models, but none is a universal winner. OpenAI’s Model Spec advises putting untrusted data in an untrusted_text block when available; otherwise, it recommends choosing YAML, JSON, or XML based on readability and escaping. JSON and XML require escaping special characters, while YAML relies on indentation.
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For any of these formats, mark where supplied content begins and ends, and tell the model how to treat that content—for example, as data to summarize rather than instructions to follow. Formatting can make a boundary clearer; it is not a security guarantee and does not, by itself, prevent prompt injection.
For application storage and transport: consider Protocol Buffers
Protocol Buffers (Protobuf) is designed for typed structured data. Google’s overview describes compact storage, fast parsing, generated code for multiple languages, and extensibility. Those properties can suit an application that needs typed records and an evolving schema across services.
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That does not make Protobuf’s binary wire representation a useful prompt format by default. Keep it behind the application boundary unless the model endpoint explicitly supports it; convert the data to an accepted textual or multimodal representation when building the model request.
For provider-specific conversation streams: follow the interface
OpenAI’s Harmony documentation describes a conversation format with special tokens for message structure and metadata. Use such a format when deliberately working with the provider-specific interface that requires it. It is not a general instruction for developers to hand-author special-token streams in ordinary API prompts.
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For connections to external tools or data: distinguish protocol from serialization
The Model Context Protocol (MCP) is an open protocol for connecting AI applications to data sources and tools. It addresses integration and connectivity; it is not a universal encoding choice for every prompt or every piece of application data.
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There is no established universal ranking of JSON, YAML, XML, or other formats for token efficiency or model accuracy. Decide using the target API and your actual workload rather than assuming one syntax is inherently better.
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| Question | Why it matters |
|---|---|
| Does the model or API natively accept or constrain this representation? | A supported interface can provide clearer behavior than asking the model to imitate a format in ordinary text. |
| Does downstream software require schema validation? | If it does, use a supported constrained-output feature where possible and still handle refusals and failures. |
| Can people read and debug it? | Readable prompts make it easier to spot confusing labels, missing fields, and unclear boundaries. |
| How does it handle arbitrary or untrusted text? | Escaping in JSON and XML and indentation in YAML affect how content is represented; explicit boundaries also help distinguish data from instructions. |
| What are the token and latency costs on representative requests? | Measure these on your actual model and inputs. The cited documentation does not establish a general efficiency winner. |
| Does the application need cross-language bindings or schema evolution? | These may favor an application serialization format such as Protobuf, independently of the model-facing format. |
| How important are interoperability and avoiding provider lock-in? | Provider-specific interfaces can be useful, but they tie some behavior to that provider’s API. |
Evaluate the format in your own system
- Map the boundary. Identify whether you are formatting prompt context, model output, tool arguments, or application storage and transport.
- Use native contracts where they fit. If you need schema-matching output or tool invocation, check the current provider documentation for the model’s supported features, limitations, and refusal behavior.
- Make prompt data legible and bounded. Choose a representation people can inspect, separate untrusted content from instructions, and tell the model how to treat that content.
- Keep application serialization in its proper layer. If your application uses a compact typed format such as Protobuf, convert it at the model boundary unless the endpoint explicitly accepts it.
- Test realistic alternatives. On representative requests, record task success, malformed or schema-invalid responses, token usage, latency, and the effort required to debug prompts. Repeat under the same conditions when comparing formats; results apply to the model, task, and inputs you tested, not to every LLM system.
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