Convert messy text to strict JSON schema. Use for emails, chat logs, unstructured input. Schemas: invoice, receipt, contact, resume.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Raw text to structure | |
| schema_name | No | Target schema: invoice, receipt, contact, resume | generic |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does convey the core transformation behavior and the 'strict' schema constraint, which indicates validation-oriented parsing. However, it does not explain what happens on invalid or ambiguous input, whether the result is a single JSON object, or whether any side effects occur.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three short sentences with no filler. It front-loads the core action, then gives usage context, then lists valid schemas. Every sentence contributes distinct information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter tool with full schema coverage, the description is close to complete: it states purpose, usage context, and supported schemas. However, there is no output schema and no annotations, so the description should more explicitly describe the return value or failure behavior. The phrase 'strict JSON schema' implies the output but does not fully specify it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters clearly. The description adds a useful list of supported schema names, but these are also repeated in the schema_name parameter description. Therefore the description adds minimal value beyond the structured schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Convert messy text to strict JSON schema') and names concrete use cases: emails, chat logs, unstructured input. It also lists the supported schemas (invoice, receipt, contact, resume), which makes the resource and scope understandable. However, it does not explicitly distinguish itself from the sibling parse_invoice, which could also handle invoices.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context on when to use the tool: for emails, chat logs, and unstructured input. It does not mention when not to use it or name alternative sibling tools, so it stops short of full routing guidance. The presence of parse_invoice as a sibling makes this exclusion more valuable than it would otherwise be.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
There is meaningful overlap between parse_invoice, structure_text, and normalize_data, especially since structure_text and normalize_data both target the same schemas and parse_invoice is a more specific version of structure_text for invoices. extract_table, get_schema, and validate_structure are distinct, but the unstructured-text-to-schema tools have unclear boundaries.
All tool names follow a consistent verb_noun snake_case pattern: extract_table, get_schema, normalize_data, parse_invoice, structure_text, validate_structure. This makes the tool set predictable and easy to navigate.
Six tools is a well-scoped size for a data structuring and validation server. Each tool addresses a meaningful step in the pipeline without excessive redundancy or unnecessary surface area.
The core workflow of extracting, normalizing, and validating structured data is covered, with schema discovery available. The main gap is the lack of a way to create or modify custom schemas, but the curated-schema approach makes this a minor limitation rather than a critical failure.
