Skip to main content
Glama

canton-ccpedia

by io.github.UnityNodes

get_discussion

Read-onlyIdempotent

Get a single thread from the official Canton Network community forum (Discourse at forum-style discussions on ccpedia.xyz) by numeric topic id: title, category, view/post counts, and the first ~15 posts. Canton-only, served from CCPEDIA's cached forum index. This is the WEB FORUM. For GitHub Discussions use get_github_discussion, for sync.global mailing-list threads use get_mailing_thread. Get the id from search results or trending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesForum topic ID from a search result, e.g. 3242 or "topic:3242". Not a doc-page id: those go to get_doc.

Schema Changelog

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

  1. Changed5 schema fields changed
    • addedInput schema / properties / id / anyOf
      Added value: +[
+  {
+    "maximum": 9007199254740991,
+    "minimum": -9007199254740991,
+    "type": "integer"
+  },
+  {
+    "type": "string"
+  }
+]
    • changedInput schema / properties / id / description
      Previous value: -"Forum topic ID"New value: +"Forum topic ID from a search result, e.g. 3242 or \"topic:3242\". Not a doc-page id: those go to get_doc."
    • removedInput schema / properties / id / maximum
      Removed value: -9007199254740991
    • removedInput schema / properties / id / minimum
      Removed value: --9007199254740991
    • removedInput schema / properties / id / type
      Removed value: -"integer"
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds contextual behavior: the result is served from a cached forum index (may be stale) and includes only the first ~15 posts. It also limits scope to Canton-only content. This goes beyond the annotations, though it doesn't cover potential error conditions.

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 four sentences, each serving a distinct function: what the tool does, its source/scope, sibling alternatives, and how to obtain the id. There is no redundant or filler language.

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?

For a simple read-only tool with one well-documented parameter and no output schema, the description provides sufficient context: return fields, source/caching behavior, scope, and relationship to sibling tools. It does not leave critical gaps for an agent to select or invoke the tool correctly.

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?

Schema coverage is 100% and the parameter description already explains the id format and disambiguates from doc-page ids. The description adds 'numeric topic id' and 'Get the id from search results or trending,' which is helpful guidance but doesn't materially change parameter semantics beyond 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 names a specific action ('Get a single thread') with a clear resource ('official Canton Network community forum') and lists the returned data (title, category, counts, first ~15 posts). It also explicitly distinguishes from sibling tools (get_github_discussion, get_mailing_thread), making the purpose unambiguous.

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?

The description explicitly states when to use this tool ('This is the WEB FORUM') and provides alternatives for other discussion types ('For GitHub Discussions use get_github_discussion, for sync.global mailing-list threads use get_mailing_thread'). It also advises where to obtain the id ('from search results or trending'), giving clear context.

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
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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