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. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context beyond annotations by stating that it returns the complete set of patterns with slug, category, name, summary, and provenance, reinforcing the openWorldHint=false semantics.
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 with no wasted words. The core purpose and output fields are front-loaded, and the usage guidance is delivered efficiently in the second sentence.
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 read-only list tool with one optional parameter, rich annotations, and an output schema, the description covers all essential decision points: what is returned, the scope, and when to choose an alternative. Nothing critical is missing.
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?
Schema description coverage is 100%, and the only parameter, `locale`, is fully documented in the schema with an enum and description. The tool description adds no extra parameter guidance, which is acceptable given the schema's completeness.
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 and resource ('List all Enterprise AI patterns'), identifies the item type ('reusable agentic design patterns'), and names the exact fields returned. It also differentiates itself from the sibling `search` tool, so an agent can tell them apart without inspecting schemas.
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?
It explicitly says when to use this tool ('browse the catalogue') and when to use the alternative (`search` when looking for a pattern that solves a problem). This gives clear routing guidance with no inference required.
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.
Most tools are cleanly separated by content type and the list_/get_ pairs are predictable. The main ambiguity is among search, search_all, and search_articles: search claims to cover the 'whole corpus' while search_all actually expands to essays, labs, claims, and the Homeric Atlas, so an agent could select the narrower search and miss content.
Every tool follows the same snake_case verb_noun pattern: calculate_*, get_*, list_*, and search_*. Even the three search variants are predictable from their suffixes, so there are no mixed naming conventions.
30 tools is above the preferred MCP size and creates real selection burden for agents, even though the multi-surface knowledge scope explains the volume. The set is systematic rather than bloated, but 25+ tools is still too many for a typical server surface.
Each content surface has browse, fetch, and search coverage, and get_related plus get_overview provide cross-cutting navigation. The only meaningful gap is that the relationship between search and search_all is not fully disjoint, which can create a dead-end if the wrong search tool is chosen first.