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canton-ccpedia

by io.github.UnityNodes

get_cip_vote_outcome

Read-onlyIdempotent

Find out whether a Canton Improvement Proposal (CIP) was actually acted on ON CHAIN, and how. Returns every DSO governance vote request whose text cites this CIP, with outcomes and dates. This answers 'was it decided', which the mailing-list tools cannot: get_cip_votes reads the discussion on the cip-vote list, this reads the ledger. Example: CIP-0116 (Featured App Locking) is cited in dozens of requests that paused apps for non-compliance. Canton ecosystem only, not Cardano or other CIP schemes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many vote requests to return, newest first. Default 20, max 100. The response states the full count either way.
cip_idYesCIP id, e.g. "CIP-0116", "116", "0116".

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / limit
      Added value: +{
+  "default": 20,
+  "description": "How many vote requests to return, newest first. Default 20, max 100. The response states the full count either way.",
+  "maximum": 100,
+  "minimum": 1,
+  "type": "integer"
+}
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark read-only/idempotent/non-destructive, and the description adds meaningful behavioral specifics: it reads the on-chain ledger, returns vote requests with outcomes and dates, and clarifies the matching mechanism ('whose text cites this CIP'). The concrete example (CIP-0116) further illustrates real-world output. No contradictions with 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 four sentences, each carrying distinct weight: purpose, return type, contrast with sibling, and scope/example. It is front-loaded with the core question and contains no filler or repetition.

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 read-only tool with full annotations and schema coverage, this description is complete: it explains what counts as a result (vote requests citing the CIP), what the output contains (outcomes and dates), how it differs from related tools, and ecosystem boundaries. No output schema is present, but the return concept is clearly described.

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% (both parameters have descriptions), so baseline is 3. The description adds value by clarifying that `cip_id` is matched against citations in vote request texts, and that `limit` returns newest-first (though schema also states this). This citation-matching context goes beyond the schema's basic type/format info.

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 opens with a specific verb ('Find out') and resource ('Canton Improvement Proposal... acted on ON CHAIN'), then details the return set ('every DSO governance vote request whose text cites this CIP, with outcomes and dates'). It explicitly contrasts with sibling `get_cip_votes` (mailing list vs ledger), making the tool's unique scope unmistakable.

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 directs when to use this tool: 'This answers was it decided, which the mailing-list tools cannot' and names the alternative (`get_cip_votes`) that reads discussion on the mailing list. It also disambiguates ecosystem scope: 'Canton ecosystem only, not Cardano or other CIP schemes.'

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