extract

meal_vision

Analyze a meal photo to itemized nutrition JSON: dishes, portions, kcal, macros with per-item confidence, reference-object scale calibration, scene-complexity detection, a disclosed calibrated kcal range, PFC balance ratios and alcohol-aware 4/4/9 cross-checks. Calibrated on 30 weighed meals (range contained truth in 26/30). Every call is a 3-run consensus with disclosed spread + item agreement. $0.05/image.

Input Schema

TableJSON Schema

Name	Required	Description
`hints`	No	cuisine/context hint, e.g. 'Japanese teishoku, small diet portions' - measurably improves accuracy on regional home-style meals
`image_url`	No
`x_payment`	No	Optional: base64 X-PAYMENT header value (x402 exact scheme, USDC on Base mainnet). Omit it to receive the payment requirements (accepts[] + docs) for this call without being charged.
`image_base64`	No

TDQS

A4.3/5.0

Behavior5/5

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

With no annotations, the description carries full burden and excels: it discloses a 3-run consensus process, calibration on 30 weighed meals (26/30 contained truth), pricing ($0.05/image), and output details like spread and item agreement. This goes far beyond typical descriptions.

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?

The description is a single dense sentence that front-loads the main function and includes many useful details (calibration, consensus, pricing). It is slightly run-on but each phrase adds value, and it remains concise enough for quick reading.

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?

There is no output schema, but the description thoroughly explains the return format (itemized nutrition JSON with per-item confidence, kcal range, PFC ratios, etc.), accuracy calibration, and payment workflow. It misses only minor details like required image parameter semantics, but overall it is very complete for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50%, and the description does not compensate for the undocumented image_url and image_base64 parameters. It does not explain how to supply the photo or clarify the relationship between these two fields. The hints parameter is well-documented in the schema, but that is already covered.

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?

The description uses a specific verb ('Analyze') and resource ('a meal photo') and details the output as 'itemized nutrition JSON' with fields like dishes, portions, kcal, macros, and per-item confidence. This clearly distinguishes it from sibling tools like ocr_image or portrait_analysis.

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 clearly implies the tool is for meal photo analysis and provides a usage hint parameter ('cuisine/context hint') that measurably improves accuracy. However, it does not explicitly state exclusions or alternatives, so it falls 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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TDQS

B3.2/5.0

Disambiguation2/5

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.

Naming Consistency3/5

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).

Tool Count2/5

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.

Completeness4/5

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.

Resources

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