list_patternsList all Enterprise AI patterns (reusable agentic design patterns) with slug, category, name, summary and provenance. Use this to browse the catalogue; use search when you are looking for a pattern that solves a problem.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Language of the returned body. Default: en. |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | ||
| results | Yes |
Changes observed during successful MCP inspections.
Output schema / properties / results / items / properties / how_to_citeAdded value: +{
+ "description": "The citation, already formed — use it verbatim.",
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false. The description adds the returned field list and browsing purpose, but does not disclose additional behavior such as pagination, ordering, or locale effects on the returned body beyond what structured fields already cover.
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?
Two tight sentences: the first front-loads the action, resource, and returned fields; the second routes the agent to the correct alternative. No filler or repetition.
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 list tool with one fully documented optional parameter, annotations, and an output schema, the description is largely complete. It could mention `get_pattern` for retrieving a single pattern's full details, but the essential browse-vs-search decision is covered.
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?
There is one optional enum parameter, locale, and schema description coverage is 100%. The schema already documents the language values and default; the description adds no locale-specific semantics beyond that. Baseline 3 is appropriate when the schema carries parameter meaning.
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?
States a specific verb and resource ('List all Enterprise AI patterns'), enumerates returned fields, and explicitly distinguishes the browse use case from the search use case. An agent can tell it apart from search and get_pattern without opening the schema.
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?
Explicitly says to use this tool to browse the catalogue and to use `search` when looking for a pattern that solves a problem. The alternative selection condition is stated directly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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