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Glama

canton-ccpedia

by io.github.UnityNodes

find_known_issues

Read-onlyIdempotent

Surface known UNRESOLVED problems matching a free-text description: forum threads with zero replies but high views, plus open GitHub issues. Answers "is anyone else hitting this?". Canton-specific. Does NOT return fixes, solutions, config, or how-to steps, and returns nothing when no open issue matches; for "how do I fix / configure / why does X happen" use semantic_search (then get_doc) instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows per source (default 5).
descriptionYesWhat you're seeing. Short prose, not a stack trace (for that use diagnose_error).

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / properties / description / description
      Previous value: -"What you're seeing — short prose, not a stack trace (for that use diagnose_error)."New value: +"What you're seeing. Short prose, not a stack trace (for that use diagnose_error)."
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations provide readOnlyHint=true and destructiveHint=false. The description adds behavioral traits: returns nothing when no match, does not return fixes/solutions, and is Canton-specific. This context is valuable beyond annotations and contains no contradiction.

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: the first fronts the core purpose with bullet-like details, the second adds exclusions and alternatives. Every word is necessary and well-structured.

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 no output schema and simple parameters, the description covers purpose, usage boundaries, alternatives, and parameter hints. It is fully complete for an AI agent to select and invoke correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents both parameters. The description adds extra guidance: for 'description' it specifies 'Short prose, not a stack trace' and directs to diagnose_error for stack traces; for 'limit' it clarifies 'Max rows per source'. This goes beyond basic schema descriptions.

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 it surfaces known unresolved problems (forum threads with zero replies but high views, open GitHub issues) matching a free-text description, answering 'is anyone else hitting this?'. It is Canton-specific and distinguishes from sibling tools like semantic_search, get_doc, and get_issue_status.

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?

Explicitly says when to use (to check for known open issues) and when not to use (does not return fixes, solutions, config, or how-to steps). Provides an alternative: for 'how do I fix / configure / why does X happen' use semantic_search then get_doc. This is excellent guidance.

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