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Glama

canton-ccpedia

by io.github.UnityNodes

list_videos

Read-onlyIdempotent

List Canton Network videos cached in CCPEDIA, filtered by channel (Canton Network, Digital Asset, Sync Insights, Canton Foundation, Daml), publish date (since), and transcript availability. CANTON-ONLY corpus. These are curated Canton ecosystem videos, not general YouTube. Returns metadata only (id, title, channel, date); call get_video with an id for the transcript.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many videos to return. Default 20, max 100.
sinceNoEarliest published_at (ISO date, e.g. "2026-01-01"). Default: no lower bound.
offsetNoSkip this many before returning, for paging past the limit. The response states the full count and echoes the offset used.
channelNoChannel name filter, e.g. "Canton Network", "Digital Asset", "Sync Insights", "Canton Foundation", "Daml". Case-insensitive substring.
has_transcriptNoIf true, only return videos with a stored transcript. If false, only those still missing one. Omit for both.

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / properties / limit / description
      Added value: +"How many videos to return. Default 20, max 100."
    • addedInput schema / properties / offset / description
      Added value: +"Skip this many before returning, for paging past the limit. The response states the full count and echoes the offset used."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only and idempotent, but the description adds valuable context: videos are 'cached in CCPEDIA', 'curated', and 'returns metadata only'. This informs the agent of data source quality and return shape, going beyond annotation basics. 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.

Conciseness5/5

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

Two sentences deliver a wealth of information: purpose, source, filters, scope, return type, and next-step tool. No fluff or redundancy. The most critical info is front-loaded.

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?

The description fully covers the tool's role, corpus boundaries, return content, and downstream action despite lacking an output schema. It complements the schema well, leaving no critical gaps for an agent to understand what this tool does.

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 already has 100% coverage with detailed descriptions for all 5 parameters. The description echoes the filter dimensions (channel, since, transcript availability) but adds no new detail beyond what the schema provides. Baseline 3 is appropriate.

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 videos from CCPEDIA with specific filters. It explicitly distinguishes this from a general YouTube search ('CANTON-ONLY corpus', 'curated Canton ecosystem videos, not general YouTube'), making it distinct from sibling tools like search_talks.

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

Usage Guidelines5/5

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

Provides explicit guidance on follow-up action: 'call get_video with an id for the transcript'. Also clarifies scope limitations ('not general YouTube') which helps an agent choose this vs. broader video search tools. The filtering criteria are directly tied to parameters, making usage clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All canton-ccpedia toolsInspect
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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