Caliper

glTF/GLB Analysis

format_gltf

Read-onlyIdempotent

Extract metadata from a glTF or GLB file.

Returns asset info, scene graph structure, mesh/material/texture
counts, vertex and index totals, feature flags (normals, tangents,
texcoords, colors, joints), primitive modes, and extensions.

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

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, ensuring the agent knows it is a safe read. The description adds valuable behavioral context by specifying what data is returned (asset info, scene graph, counts, feature flags, etc.) and the payment requirements via x402 or MPP. This goes beyond the annotations without contradicting them.

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 concise: two sentences of core purpose and output, plus one sentence on payment and privacy. Every sentence earns its place, and the most important information is front-loaded. No irrelevant filler.

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?

With no output schema, the description fully compensates by listing exact return contents (counts, feature flags, primitive modes, extensions). It also covers payment prerequisites and file size limits, making it complete for an agent to decide when and how to invoke.

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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description does not add significant parameter-level detail beyond what is in the schema, but it does mention payment methods which relate to the payment parameter. This meets the baseline for schema-heavy documentation.

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 clearly states 'Extract metadata from a glTF or GLB file.' This provides a specific verb (extract), resource (glTF/GLB file), and the tool name and title align perfectly. It distinguishes itself from siblings like format_obj and format_stl by targeting a specific format.

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 gives clear context: it is for glTF/GLB files. It also references format_auto for payment flow details, which provides some cross-tool guidance. However, it does not explicitly state when not to use it or mention alternative format-specific tools, so there is a minor gap in exclusions.

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.

Resources

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