Search CCPEDIA's indexed Canton talks/videos (YouTube transcripts). Canton-specific. Returns matches across title + transcript with a short snippet around the hit.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 5). | |
| query | Yes | Free-text query. Appears in title or transcript. | |
| offset | No | Skip this many before returning, for paging past the limit. The response states the full count and echoes the offset used. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / offsetAdded value: +{
+ "default": 0,
+ "description": "Skip this many before returning, for paging past the limit. The response states the full count and echoes the offset used.",
+ "maximum": 9007199254740991,
+ "minimum": 0,
+ "type": "integer"
+}Input schema / properties / query / descriptionPrevious value: -"Free-text query — appears in title or transcript."New value: +"Free-text query. Appears in title or transcript."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds useful behavioral detail: it searches title and transcript, and returns a snippet around the hit. This goes beyond annotations and helps the agent understand what the tool does with the query.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences: the first states what is searched and scope, the second explains matching and snippet. Every word contributes meaning, and it is front-loaded with the core purpose. No wasted words or unnecessary repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with three well-documented parameters and safety annotations, the description is largely complete: it states the dataset, scope, search fields, and snippet behavior. It does not describe the full return structure, but the offset parameter's schema description covers response count details, and no output schema is present, so the description carries enough weight for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters (query, limit, offset) have descriptions in the JSON schema, so schema coverage is 100%. The tool description does not add extra parameter semantics beyond what the schema already provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search'), the resource ('CCPEDIA's indexed Canton talks/videos'), and the scope ('Canton-specific'), which distinguishes it from the broader 'search' and 'semantic_search' siblings. It also specifies the matching fields ('title + transcript') and output format ('short snippet around the hit').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating 'Canton-specific' and limiting to talks/videos, but it does not explicitly contrast with alternatives like 'search' or 'semantic_search', nor does it state when not to use this tool. No exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.