Fetch metrics for one specific Featured App by its app_id (contract id from the Scan API). Returns name, category, TVL, 24h volume, user count, and last activity from the Featured Apps registry. Canton-specific. TVL / volume / users may be null when only the Scan API source is available.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | Featured-app contract id, or a unique prefix of it as shown by list_featured_apps. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / app_id / descriptionPrevious value: -"Featured-app contract id (long hex string from list_featured_apps)."New value: +"Featured-app contract id, or a unique prefix of it as shown by list_featured_apps."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds useful context by listing return fields and noting 'TVL / volume / users may be null when only the Scan API source is available,' which goes beyond the annotations and helps set expectations for data completeness.
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 three sentences, front-loaded with the core action, followed by return fields and a caveat. Each sentence serves a distinct purpose with no redundancy or filler.
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 tool with one param and no output schema, the description is remarkably complete: it states the scope (one specific app), the source (Featured Apps registry), the exact fields returned, and the nullability caveat. Combined with strong annotations, nothing essential 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?
The schema already provides excellent coverage (100%) for app_id, describing it as a 'Featured-app contract id, or a unique prefix of it as shown by list_featured_apps.' The description rephrases 'app_id (contract id from the Scan API)' but adds no material new meaning beyond the schema.
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 states a specific verb+resource+scope: 'Fetch metrics for one specific Featured App by its app_id.' It clearly distinguishes from siblings like list_featured_apps by focusing on a single app and enumerates the exact metrics returned.
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 when to use this tool (when you have a specific app_id and want metrics for that app) and references list_featured_apps in the schema for obtaining the ID. However, it does not explicitly name alternatives or state when not to use it, leaving some room for interpretation.
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.