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canton-ccpedia

by io.github.UnityNodes

get_foundation_info

Read-onlyIdempotent

Retrieve official Canton Foundation pages (canton.foundation): team and board bios, working groups, membership process, grants program. Free-text topic search over CCPEDIA's index of the canton.foundation site, returning matched-page snippets (not the curated knowledge base). Canton-only. Use when a user asks about Foundation people, governance structure, or member/grant processes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many matching sections to return. Default 3, max 10.
topicYesFree-text topic, e.g. "team", "executive director", "working groups", "membership", "grants", "board".

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / limit / description
      Added value: +"How many matching sections to return. Default 3, max 10."
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: it returns 'matched-page snippets' rather than full documents, and it clarifies scope ('Canton-only', 'not the curated knowledge base'). This enriches the agent's expectations without contradicting 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 three sentences, front-loaded with the main action and scope, then usage guidance. Every sentence adds distinct value: what it retrieves, how it searches, and when to use it. No fluff or redundancy.

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 two well-documented parameters and no output schema, the description covers purpose, source, scope, return format, and usage context. It is fully self-contained and matches the complexity of the tool. For example, it clarifies what kind of snippets to expect and limits applicability to Canton-specific queries.

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?

Both parameters (topic, limit) are fully described in the schema with example values and constraints, so schema coverage is 100%. The description adds 'Free-text topic search' but does not elaborate beyond the schema, meaning its contribution is neutral. 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 states a specific verb ('Retrieve') and resource ('official Canton Foundation pages (canton.foundation)'), listing concrete content types (team and board bios, working groups, membership process, grants program). It also differentiates from sibling tools by noting it searches CCPEDIA's index rather than the curated knowledge base and is Canton-only.

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 states when to use the tool: 'Use when a user asks about Foundation people, governance structure, or member/grant processes.' It also provides a light exclusion ('not the curated knowledge base'), but does not name alternative tools or when-not-to-use scenarios, so it stops short of full guidance.

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