Pattern-search the Featured Apps catalogue by free-text query across name, category, and description. Returns the best matches in name+category, falling back to description. Canton-specific.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 10). | |
| pattern | Yes | Free text, e.g. "AMM", "wallet", "DeFi vault", "validator". |
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?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable behavioral details: the fallback search logic (matching first by name+category then description) and Canton-specific scope, which go beyond 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 covering purpose, search fields, fallback, and scope. No filler or redundant information.
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 tool's simplicity (2 parameters, no output schema), the description adequately explains behavior. It could mention result format or pagination, but with schema covering limit and schema coverage 100%, it is mostly complete.
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 coverage is 100% with descriptions for both parameters (pattern and limit). The description does not add further parameter semantics beyond what the schema provides, resulting in a baseline score.
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 performs a pattern search across the Featured Apps catalogue using free-text, specifying the fields searched (name, category, description) and the fallback logic. It also notes Canton-specificity, distinguishing it from generic search tools like 'search' and 'semantic_search'.
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 use for pattern-based queries on the Featured Apps catalogue, but does not explicitly state when to use this tool versus alternatives such as 'list_featured_apps' or 'semantic_search'. No guidance on when not to use is provided.
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.
Many tools have overlapping search/retrieval functionality (search, semantic_search, full_context, search_community, search_github_issues, etc.), and the CIP-specific variants (get_cip, get_cip_history, get_cip_votes, get_cip_mentions, get_cip_citations) are numerous and subtly differentiated. Despite cross-references in the descriptions, the boundaries are fine-grained and an agent is likely to misselect among the 8+ search tools or the 8+ CIP tools.
All tool names follow a consistent verb_noun snake_case pattern (get_x, list_x, search_x, find_x). Mixed styles or camelCase are absent, and the verb choice (get, list, search, find, detect, compare) is semantically appropriate to each action, making the naming highly predictable.
With 88 tools, the surface is extremely overgrown for a single server, far exceeding the 25+ 'too many' threshold and approaching the 50+ 'extreme mismatch' category. Even for a comprehensive ecosystem knowledge base, this creates a massive selection burden and makes the tool set unwieldy for agents.
The server covers the full Canton ecosystem: docs, forum, mailing lists, GitHub, CIPs, governance, validators, versions, deprecations, security, and media. There are no glaring gaps in the knowledge domain; every major resource type has retrieval and analysis tools, making the coverage exhaustive with no obvious dead ends.