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canton-ccpedia

by io.github.UnityNodes

list_featured_apps

Read-onlyIdempotent

List Canton Network Featured Apps from the live Scan-API-sourced catalogue. Filter by category (when known) and choose ordering. Canton-specific. Use for "what dApps are live on Canton?" or to discover providers offering a particular service. Returns name, provider/app_id, category, last_activity, url.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort key (default: recent).
limitNoMax rows (default 25).
categoryNoFilter by category, e.g. "Validator", "DeFi", "Tokenized Assets" (case-insensitive substring match).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint, and the description adds that the source is a live Scan-API catalogue and lists the returned fields (name, provider, category, etc.), providing useful behavioral context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence followed by two bullet points, conveying all essential information without unnecessary words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (read-only, no output schema), the description together with the annotations completely covers what the tool does, when to use it, its parameters, and its return fields. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers all three parameters with descriptions, and the description mentions filtering by category and choosing ordering, but does not add significant extra meaning beyond what the schema already provides (e.g., the case-insensitive substring match is already in the schema).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists Canton Network Featured Apps from a live Scan-API catalogue, with filtering and ordering. It distinguishes from siblings like 'find_apps_by_pattern' by specifying the exact source and that it's Canton-specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool, e.g., 'what dApps are live on Canton?' or to discover providers. It provides clear context but does not explicitly mention when not to use or point to alternatives.

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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TDQS

A3.7/5.0
Disambiguation2/5

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.

Naming Consistency5/5

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.

Tool Count1/5

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.

Completeness5/5

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.

Resources