Normalize an IT hardware part number into its canonical form. Handles HPE (B21/001/spare), Dell (400-XXXX, DP/N), and IBM/Lenovo (FRU) formats. Critical for deduplication and accurate lookups.
| Name | Required | Description | Default |
|---|---|---|---|
| partNumber | Yes | Part number is required |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It conveys the transformation behavior and supported formats, but does not disclose what happens for unsupported inputs, the exact canonical format, or the return value structure, leaving some behavioral ambiguity.
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 three concise sentences, each serving a distinct purpose: defining the action, listing supported formats, and identifying the use case. There is no redundancy or filler.
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 simple one-parameter normalization tool without an output schema, the description covers purpose, formats, and use case. It does not specify error handling for invalid inputs or the return format, but these gaps are not critical given the tool's simplicity.
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 only states 'Part number is required' with minimal detail, while the description adds meaningful semantics by indicating the input can appear in HPE, Dell, or IBM/Lenovo formats. This helps the agent understand acceptable input formats beyond 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 normalizes an IT hardware part number into its canonical form and explicitly lists the supported vendor formats (HPE, Dell, IBM/Lenovo). This distinguishes it from siblings like validate-pn or get-part, which have different purposes.
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 notes that normalization is 'critical for deduplication and accurate lookups,' giving clear context for when this tool is useful. It does not explicitly name alternatives or exclusions, but the use case is well implied.
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.
Each tool has a clearly distinct purpose: retrieving a single part record, finding substitutes, normalizing part numbers, batch searching, and validating existence. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern (e.g., get-part, normalize-pn, validate-pn) using snake_case. The naming is predictable and easy to understand.
With 5 tools, the server is well-scoped for a read-only parts catalog. Each tool serves a essential function without being too few or overly numerous.
The tool set covers the core operations needed for a parts table: record retrieval, cross-reference lookup, normalization, batch existence check, and validation. There are no obvious gaps given the read-only design.
