List service categories for a provider (e.g. 'aws' -> ['compute', 'database', ...]).
Args: provider: Provider name from list_providers (e.g. 'aws', 'gcp', 'k8s').
| Name | Required | Description | Default |
|---|---|---|---|
| provider | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, covering the safety profile. The description adds value by showing a sample return format and the dependency on list_providers, providing behavioral context beyond the 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?
The description is extremely concise—two sentences plus an Args line—with the core purpose front-loaded. Every element (purpose, example, parameter documentation) earns its place, and there is no wasted wording.
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 tool with one parameter and an output schema present, the description covers the tool's purpose, the parameter's provenance, and the expected output format via example. This is sufficient for an agent to select and invoke the tool correctly.
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 input schema only specifies 'provider' as a string with 0% description coverage, but the description fully documents the parameter: it explains what the value represents (provider name), where to obtain it (list_providers), and gives example values ('aws', 'gcp', 'k8s'). This completely compensates for the schema gap.
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 lists service categories for a provider, with a concrete example ('aws' -> ['compute', 'database', ...]). However, it does not explicitly distinguish from the sibling tool 'list_categories', so the distinction is implicit.
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 after list_providers by specifying that the provider name comes from list_providers, which is a useful prerequisite. However, it offers no explicit guidance on when not to use the tool or mentions any alternative tools.
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 serves a distinct purpose: browsing (list_providers, list_services, list_nodes, search_nodes), equivalence lookup (find_equivalent, list_categories), and rendering in three different syntaxes (render_diagram, render_mermaid, render_plantuml). No two tools overlap in function, and descriptions clearly differentiate when to use each.
All tool names follow a consistent verb_noun pattern with lowercase snake_case: list_* for browsing, render_* for output generation, find_equivalent, and search_nodes. The pattern is predictable and aids agent understanding.
With 9 tools, the server is well-scoped: 4 browsing tools, 2 equivalence tools, and 3 rendering tools cover the diagram creation workflow without bloat. Each tool earns its place.
The tool surface supports the full workflow: discover providers/services/nodes, search for equivalent roles, and render diagrams in Python, Mermaid, or PlantUML. No obvious gaps exist for the stated purpose.