Create a durable CSV, XLSX, JSON, feed, API, document, or manual catalog import. Supply parsed rows for immediate validation, or an artifact/source reference to enter the mapping workflow.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | No | ||
| mapping | No | ||
| source_url | No | ||
| source_type | Yes | ||
| idempotency_key | Yes | ||
| source_artifact_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
| catalog_import | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates that the operation creates a durable import and mentions downstream effects like validation and mapping. It does not contradict the annotations (idempotentHint=true, destructiveHint=false). The description adds context about the workflow without repeating annotation details, which is appropriate given the annotations already cover idempotency.
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?
The description is concise, consisting of two sentences that pack substantial information without redundancy. It is well-structured, clearly stating the action and then the two primary usage modes.
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?
Given the complexity of the schema (nested objects, many fields) and the existence of an output schema, the description provides sufficient high-level context about the tool's purpose and workflow. It mentions validation and mapping, which gives the user a sense of what happens after creation. It does not need to explain return values since an output schema exists.
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?
The description adds meaning to a few key parameters (rows and source_artifact_id) by explaining their roles in validation vs. mapping. However, it does not clarify other important parameters like source_type, idempotency_key, mapping, or source_url. With a schema coverage of 0%, the description only partially compensates for the lack of parameter explanations.
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 clearly states the tool's primary function: creating a durable catalog import across various source types (CSV, XLSX, JSON, feed, API, document, manual). It distinguishes it from sibling tools by focusing specifically on catalog imports and mentioning the two modes of operation (parsed rows vs. artifact/source reference).
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 actionable guidance on how to use the tool: either supply parsed rows for immediate validation or provide an artifact/source reference to enter the mapping workflow. This gives clear conditions for usage, though it does not explicitly contrast with alternatives like upsert_supplier_catalog.
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.
Every tool targets a distinct resource, role, or action. For example, list_orders versus list_supplier_orders clearly separates buyer and supplier views, and update_supplier_order versus update_supplier_order_issue versus update_supplier_order_return handle different concerns despite similar names. Rich descriptions eliminate ambiguity.
All tool names consistently use snake_case with a verb_noun pattern (e.g., create_project, list_orders, update_supplier_capabilities, search_products). The style is uniform across reads, writes, lists, and searches, making the API predictable.
With 55 tools, the server far exceeds the typical well-scoped range of 3-15. Even for a broad supply chain platform, 50+ tools hits the rubric's 'extreme mismatch' threshold. The domain is comprehensive, but the sheer number overwhelms and likely complicates agent tool selection.
The tool surface covers the full lifecycle from project creation, BOM management, sourcing, quoting, planning, checkout handoff, orders, returns, issues, warranties, catalog updates, supplier profiles, and validation. It handles buyer and supplier sides with appropriate state transitions, and includes meta tools for connection and schema guidance. No obvious critical gaps exist.