Free recorded sample response for a paid product (from real production runs, marked sample:true) - see the exact response shape before paying anything. Available: ocr, enrich, verify, structured, jp-company, document-pipeline, pdf-info, meal-vision, portrait-analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It reveals that samples come from real production runs and are marked sample:true, and it promises to expose the exact response shape. This covers the key read-like behavior and inherent nature of the sample, though it does not explicitly state error handling or non-mutating characteristics.
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 concise, with two sentences that pack high information density. The first sentence front-loads the purpose and value proposition, and the second efficiently lists available product identifiers. There is no redundant or filler content.
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 simple tool with one parameter and no output schema, the description provides complete context: what the tool does, why it is useful, and which product values are accepted. The promise of seeing the exact response shape addresses the lack of an output schema, making the description sufficient for an agent to know what to expect.
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?
The input schema only defines 'product' as a string with no description, leaving it completely ambiguous. The description compensates by enumerating valid values: ocr, enrich, verify, structured, jp-company, document-pipeline, pdf-info, meal-vision, portrait-analysis. This is essential for correct invocation and significantly adds meaning beyond the 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 defines the tool as a free recorded sample response retriever for paid products, using the specific verb 'get' and resource 'sample response'. It also differentiates from sibling tools by listing the specific product categories it covers and framing itself as a preview mechanism rather than the actual product execution.
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 implies the tool should be used to preview a paid product's response before paying, providing clear context for when to invoke it. It does not explicitly state alternatives or exclusions, but the phrase 'before paying anything' effectively communicates its intended use case relative to the actual paid product tools.
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.
Multiple tools have unclear boundaries: document_pipeline and parse_document both parse PDFs, portrait_analysis and style_profile both perform color analysis, enrich_company and jp_company_profile both resolve company profiles, and jp_payroll_suite bundles capabilities that already exist as separate tools. Descriptions are detailed, but an agent must carefully compare several near-synonyms before selecting.
All names are snake_case, but the conventions are mixed: get_* for metadata, jp_* for Japan-specific calculations, pdf_* for PDF operations, plus standalone nouns like meal_vision and style_profile. The prefixes help readability, but there is no uniform verb_noun pattern and ordering is inconsistent (quote_parse vs parse_document).
35 tools is far above the well-scoped 3-15 range and indicates an aggregator/marketplace rather than a focused server. Even with clear individual descriptions, the sheer breadth across OCR, PDF, Japan tax, style, and marketplace functions makes the tool set feel heavy and harder to navigate.
Within its broad marketplace scope, the server is fairly complete: paid products have free samples (get_sample), quotes (quote_parse), receipt retrieval (get_receipt), and discovery via get_catalog and search_x402_services. PDF and Japan tax coverage are extensive, though the wide domain spread means no single area feels fully exhaustive.