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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds useful behavioral context by specifying the exact return fields and that it lists ALL patterns, which goes beyond the annotations. No contradictions.
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 two sentences, front-loaded with the action and output fields, followed by succinct usage guidance. Every word adds value with no unnecessary fluff.
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 simplicity of the tool (list operation, one optional parameter), the comprehensive annotations, and the presence of an output schema, the description fully covers the essential context. It includes the purpose, output fields, and usage alternatives, leaving no critical gaps.
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 description for the sole parameter 'locale' is complete (100% coverage) with a clear description and enum values. The tool description adds no extra meaning to the parameter, so the baseline of 3 is appropriate.
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 action (List), the resource (Enterprise AI patterns), and the output fields (slug, category, name, summary, provenance). It distinguishes itself from sibling tools by explicitly contrasting with `search` and positioning itself as a catalogue browsing tool.
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 explicitly says when to use this tool ('Use this to browse the catalogue') and when to use the alternative (`use 'search' when you are looking for a pattern that solves a problem`). This provides clear usage guidance and differentiates from the most similar sibling.
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 operation and identifier: search is the entry point, list_* returns browsing summaries, get_* returns a single unit, get_related traverses the graph, and get_overview maps the corpus. Even the similar get_homeric_* trio is cleanly separated by episode/place/route.
All names follow snake_case verb_noun: get_* for singular retrieval, list_* for enumeration, plus search. get_related and get_overview are the only deviations but remain predictable read operations.
21 tools is above the typical 3-15 range, but the count is justified by the number of distinct corpora and the consistent list/get pairing for each; there are no redundant tools, so it is only slightly heavy.
The server offers a complete read-side lifecycle for this knowledge corpus: overview, search, list, get, and graph traversal. For a read-only knowledge server, there are no obvious dead ends; coverage of claims, patterns, architectures, governance, handbook and Homeric atlas is thorough.