Caliper

PCD Analysis

format_pcd

Read-onlyIdempotent

Extract metadata from a PCD point cloud file.

Returns point count, field definitions, data format, organization,
viewpoint, feature flags (RGB, intensity, normals, curvature),
bounding box, centroid, and point density estimate.

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

A4.1/5.0

Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint, so the description need not restate safety. It adds behavioral context about payment requirements (x402/MPP) and the return categories, which are meaningful for an agent deciding whether to invoke the tool and anticipating conditional payment steps. No contradiction with annotations.

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 well-structured: purpose, returns, payment info. It is fairly concise, though the privacy policy URL adds slight noise. Every sentence carries useful information, with the return list and payment details being genuinely helpful.

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?

Given the rich schema and absence of an output schema, the description compensates by listing key return fields. It also mentions payment methods, which is important for operational context. However, it relies on a cross-reference to format_auto for payment specifics and does not explicitly state the need to provide one of file_b64 or file_url, though this is covered by schema descriptions.

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 coverage is 100%, with each parameter having a detailed description. The tool description does not add extra parameter-related meaning, so the baseline of 3 is appropriate. It only mentions return aspects, not parameter semantics.

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 a PCD point cloud file,' which is a specific verb+resource combination. It clearly distinguishes itself from sibling format_* tools by naming the PCD format and enumerating the metadata returned, making its purpose unambiguous.

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 implies clear usage context: use for PCD files when metadata extraction is needed. It does not explicitly contrast with alternatives like format_ply or format_las, but the format-specific scope is sufficiently evident. The reference to 'See format_auto for payment flow details' is a cross-reference about payment, not usage, so it does not serve as an alternative-selection guideline.

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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