Surface signals of what the Canton community thinks about a topic: forum thread reply ratios, mailing list debate volume, and the first reply on the most-viewed thread. Canton-specific. Not sentiment-analysis; a structured roundup the caller can summarise.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Topic to assess. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to cover safety. It adds value by clarifying that the output is a structured roundup of quantitative signals, not a subjective interpretation, which helps set expectations. It also discloses the specific data sources (forum threads, mailing lists, most-viewed thread).
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?
Two sentences, front-loaded with the primary action and followed by specific examples and an explicit limitation. No fluff or redundant restatements of the schema.
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?
Despite lacking an output schema, the description enumerates the three signal types the tool returns, giving the caller a clear expectation of the result. It also notes the caller can summarize it, covering practical use. It doesn't specify exact response format or pagination, but for a simple single-param read-only tool this is adequate.
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?
Schema has 100% coverage for the single 'topic' parameter with a description ('Topic to assess'). The tool description does not add any format guidance or examples, but with full schema coverage, 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 opens with a specific verb 'Surface' and a clear object: what the Canton community thinks about a topic, itemizing concrete outputs (forum reply ratios, mailing list debate volume, first reply on most-viewed thread). It explicitly distinguishes itself from sentiment analysis and being Canton-specific, which differentiates it from siblings like get_discussion or search_community.
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?
It states when to use it: to assess community opinion on a Canton topic, and provides an explicit exclusion ('Not sentiment-analysis'). It doesn't name specific alternative tools, but the context (Canton-specific, structured roundup) makes its niche clear. No explicit when-not beyond sentiment analysis.
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.