Caliper

OBJ Analysis

format_obj

Read-onlyIdempotent

Extract metadata from an OBJ file.

Returns vertex/normal/texcoord/face counts, triangle/quad/polygon
breakdown, material and group counts, bounding box, surface area,
and triangulation status.

Payment via x402 (USDC on Base) or card via MPP (Stripe). See format_auto
for payment flow details. Privacy policy: https://caliper.fit/privacy

Input Schema

TableJSON Schema

Name	Required	Description
`payment`	No	Payment proof as a JSON string. Set this when retrying after a payment_required response. For x402: must contain 'transaction' (on-chain tx hash), 'network', and 'priceToken' from the payment_required response. For MPP: must contain 'challenge' and 'payload' from the org.paymentauth/credential flow. Default: null (omit on first call; set only when retrying with payment).
`file_b64`	No	Base64-encoded file content. Max 200KB decoded. Use file_url for larger files to avoid consuming model context window budget. Default: null (omit if providing file_url instead).
`file_url`	No	HTTP/HTTPS URL of the geometry file to analyze. Preferred for large files (over 200KB). The file format is detected from the URL path extension, so the filename parameter is not needed when using file_url. Max 100MB. Default: null (omit if providing file_b64 instead).
`filename`	No	Original filename with extension (e.g. 'model.stl'). Required for format detection when using file_b64. Not needed when using file_url (format is detected from the URL path). Default: null.

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already provide readOnly and idempotent hints, so the description doesn't need to restate them. It adds valuable behavioral context by disclosing the payment requirement (x402/MPP) and pointing to format_auto for flow details, which goes beyond the annotations and informs the agent of a necessary precondition.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two compact paragraphs with no fluff. The first sentence front-loads the purpose, the following sentences list return metrics and payment info concisely. Every sentence serves a clear informational role, and the privacy policy line is a minor but acceptable addition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, return values, and payment requirements, complementing the detailed schema well. It could more explicitly clarify that this tool is for OBJ files only versus the other format_* siblings, but the tool name and first line already convey that, making the description largely complete for an agent to select and invoke correctly.

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

Parameters3/5

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

All four parameters have detailed descriptions in the schema, achieving 100% coverage. The tool description itself does not add extra parameter-level meaning, so it neither compensates for gaps nor repeats schema content; this aligns with the baseline score.

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 opens with 'Extract metadata from an OBJ file', which pairs a specific verb with a well-defined resource. It then enumerates concrete return metrics (vertex counts, bounding box, surface area, etc.), clearly distinguishing it from sibling tools that target other file formats.

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 establishes when to use the tool: for OBJ files, by explicitly naming the format. It also references format_auto for payment flow details, which is useful context. However, it does not explicitly name alternative tools for other formats or state when not to use this tool, so it stops short of full exclusionary guidance.

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

A4.4/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose with no ambiguity. The general-purpose format_auto and format_batch tools cover multiple formats, while the format-specific tools (format_gltf, format_las, etc.) target individual formats, and feature_request serves a completely different administrative function. The descriptions clearly differentiate between auto-detection, batch processing, format detection, and format-specific analysis.

Naming Consistency5/5

All tools follow a consistent snake_case naming pattern with clear verb_noun structure. The format_ prefix is used consistently for 9 out of 10 tools (format_auto, format_batch, format_detect, format_gltf, etc.), while feature_request follows the same naming convention for the remaining tool. There are no deviations in naming style or convention.

Tool Count5/5

With 10 tools, this is well-scoped for a geometry file analysis server. Each tool earns its place by covering different aspects of the domain: general analysis (format_auto), batch processing (format_batch), format detection (format_detect), format-specific analysis (7 tools for different formats), and feature requests (feature_request). The count is neither too sparse nor overwhelming for the domain.

Completeness4/5

The tool surface provides excellent coverage for geometry file metadata extraction across multiple formats, with both general and format-specific tools. The inclusion of batch processing and format detection adds useful workflow support. The only minor gap is the lack of tools for actual mesh manipulation, repair, or conversion operations, but the server's stated purpose appears focused on analysis rather than modification, and feature_request allows users to request missing capabilities.

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