get_catalog_snapshot_statusReturn fixture-backed joined catalog counts, validation status, source provenance, live connector readiness, and non-authoritative product-truth warning.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: 'fixture-backed' hints at test data, and 'non-authoritative product-truth warning' discloses data reliability. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence that front-loads the verb and packs all return items without fluff. Every word contributes meaning. Perfectly concise for a zero-parameter tool.
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?
No output schema exists, but the description lists the key return elements. It lacks details on exact structure or field types, but for a status endpoint with no parameters, the coverage is reasonable. Could be slightly more specific about the 'non-authoritative' warning.
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?
No parameters exist (0 params), so baseline is 4. The description focuses entirely on output semantics, which is appropriate given 100% schema coverage for parameters.
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 uses a specific verb 'Return' and clearly enumerates the exact data items (counts, validation status, source provenance, live connector readiness, warning). It distinguishes this tool from siblings by highlighting its comprehensive status nature, and no sibling overlaps with this combination.
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 provides no information about when to use or avoid this tool, nor does it mention any alternatives among the listed siblings (e.g., get_shopify_readonly_config_status, lookup_catalog). Agents must infer usage from the name alone.
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.
Several tools have significant overlap in purpose and trigger keywords. calculate_field_of_view, match_lens_to_sensor, and get_lens_distortion_profile all share nearly identical use-case descriptions for FOV/sensor requests, while search_catalog, search_lens_catalog, and lookup_catalog all provide search/lookup functionality with subtle differences that are easy to confuse.
All tool names follow a consistent verb_noun snake_case pattern (e.g., calculate_field_of_view, create_cart, get_product, read_shopify_products, submit_rfq). No mixing of styles or unpredictable naming conventions.
With 21 tools, the surface is noticeably heavy for a lens finder and FOV calculator, especially when several tools serve status/config purposes (get_catalog_snapshot_status, get_shopify_readonly_config_status, get_shopify_ucp_readiness). It sits in the 'feels heavy' range from the rubric.
The tool set covers the full lifecycle: catalog search, product details, sensor specs, FOV calculation, lens matching, distortion profiles, Shopify carts, purchase handoff, and RFQ submission. Minor gaps exist (e.g., no delete_cart or bulk sensor listing), but core workflows are well-supported.