Append timestamped supplier availability evidence and update current catalog state by supplier SKU without rewriting historical snapshots.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| updated_count | Yes | ||
| updated_catalog_item_ids | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a write operation (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds valuable context by explicitly stating the operation 'without rewriting historical snapshots', which clarifies that previous availability data is preserved. This is an important behavioral trait beyond what annotations provide.
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 a single, compact sentence that front-loads the primary verbs and resource. Every phrase contributes meaning: 'Append timestamped', 'update current catalog state', 'by supplier SKU', 'without rewriting historical snapshots'. There is no redundancy or wasted words.
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 input schema (array of up to 500 items with detailed properties), the description provides the core behavioral model and the key guarantee of not rewriting history. The existence of an output schema and a well-structured input schema reduce the burden on the description, but it still does not mention item limits, status values, or potential failure modes.
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 schema has a rich structure (items array with required supplier_sku, status, observed_at, plus optional fields), but the description only hints at 'by supplier SKU'. With 0% schema description coverage, the description fails to explain the array structure, the meaning of statuses, or the timestamping mechanism, leaving the agent reliant solely on the schema.
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 purpose: to append timestamped supplier availability evidence and update current catalog state, scoped by supplier SKU. It uses specific verbs (append, update) and resources (supplier availability evidence, catalog state), and the mention of preserving historical snapshots distinguishes it from sibling update/upsert tools.
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 implies when to use the tool: when you have timestamped availability observations to append and want to update the current state without overwriting history. However, it does not explicitly exclude alternatives like update_supplier_catalog or upsert_supplier_catalog, nor does it list when not to use it.
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.