Read the current revision of one project BOM, including hierarchy, quantities, sourcing state, and interface dependencies.
| Name | Required | Description | Default |
|---|---|---|---|
| bom_id | Yes | ||
| project_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| bom | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, setting a clear safety baseline. The description adds valuable behavioral context by specifying the output granularity: it returns hierarchy, quantities, sourcing state, and interface dependencies, which would not be known otherwise. It also notes 'current revision,' distinguishing from version history or draft access, which enriches the interaction model.
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?
A single, precisely worded sentence that packs in the tool's purpose, scope, and return contents without any wasted words. Every phrase adds information. This is a model of efficient communication.
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?
For a low-complexity read tool with an output schema, the description is sufficiently complete: it tells the agent what the tool does and outlines the semantic groups in the output (hierarchy, quantities, sourcing state, dependencies). The presence of an output schema obviates the need to detail return values. The only minor gap is not indicating pagination or large response handling, but that is beyond the scope of a typical description.
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?
With 0% schema description coverage, the description must clarify the two parameters. It only says 'one project BOM,' which suggests bom_id but leaves project_id's role ambiguous and fails to explain the relationship between the two (e.g., project_id scopes the BOM). The description does not compensate for the lack of parameter details in 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 uses a specific verb 'Read' and a clear resource, 'the current revision of one project BOM,' and enriches it with the kind of data returned (hierarchy, quantities, sourcing state, interface dependencies). It clearly differentiates from sibling read tools like get_project or get_requirement_schema by focusing on BOM revision retrieval. This is a model example of a clear, specific, and distinct purpose.
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 usage for reading a single BOM but does not provide explicit guidance on when to choose this tool over alternatives like get_project or get_purchase_plan. There is no mention of exclusions, prerequisites, or typical use cases beyond the obvious read action. The intended use is understandable but relies on inference rather than explicit direction.
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.