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canton-ccpedia

by io.github.UnityNodes

get_kb_drift

Read-onlyIdempotent

Report where the Foundation Build-on-Canton KB snapshot (which CCPEDIA syncs daily from github.com/canton-network-devs/Build-on-Canton-MCP) diverges from CCPEDIA's live signals (github_releases, mailing_messages). Returns each drifted (category, key) with the KB value, the live value derived right now, the drift age in days, and severity (info | warn | stale). Use this when a user or another agent quotes a Canton SDK / Splice / DPM / Daml version from foundation_kb and you need to confirm whether it is still current. CCPEDIA-specific transparency layer: no other Canton MCP server exposes this kind of cross-source quality audit. Canton/Daml/Splice ecosystem only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refreshNoIf true, re-run the validator before returning the report. Default false uses the most recent stored findings.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds behavioral detail: it can optionally re-run the validator via the 'refresh' parameter, and returns drift age and severity. No contradictions with annotations. This fully informs the agent of behavioral traits beyond the structured info.

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

Conciseness4/5

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

The description is reasonably concise and front-loaded with the core purpose ('Report where... diverges...'). It uses three sentences followed by a usage note and a unique positioning statement. Every sentence adds value, though it could be slightly shorter without losing clarity.

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

Completeness4/5

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

Given the tool's complexity (cross-source drift detection) and lack of output schema, the description adequately details return fields (category, key, KB value, live value, age, severity). Annotations cover safety and idempotency. The description is complete enough for an agent to decide when to use and what to expect.

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?

With only one parameter and 100% schema description coverage, the schema already explains the 'refresh' parameter. The description does not add new meaning to the parameter beyond what the schema provides (default false, stored findings). Baseline 3 is appropriate as the description adds no extra parameter insight.

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 states 'Report where the Foundation Build-on-Canton KB snapshot diverges from CCPEDIA's live signals' with specific verb 'report' and resource 'KB drift'. It distinguishes from siblings like detect_drift by noting 'no other Canton MCP server exposes this kind of cross-source quality audit', making the tool's unique value clear.

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 says 'Use this when a user or another agent quotes a Canton SDK / Splice / DPM / Daml version from foundation_kb and you need to confirm whether it is still current.' This provides clear context for when to invoke. However, it does not mention when not to use or mention alternatives, slightly reducing the score from 5.

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