Surface ecosystem gaps: high-view forum threads with zero replies (unanswered demand), mailing threads that got no reply, and open Dev Fund proposals with no label assigned (waiting for a SIG or champion). Canton-specific. Use to find "where the ecosystem needs help". For per-category funding levels use get_funding_landscape.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows per gap type (default 5). |
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, idempotentHint=true, and destructiveHint=false, so the agent knows it's a safe read. The description adds behavioral context beyond annotations by specifying the exact sources of gaps (forum, mailing list, proposals) and the 'Canton-specific' constraint. It doesn't describe return format, but the presence of strong annotations lowers the bar.
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 three sentences, front-loaded with the core purpose, then provides concrete examples, a usage intent, and an alternative. Every sentence earns its place with no redundancy.
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?
Given the tool's low complexity (one optional parameter, no output schema), the description is complete: it states the three gap categories, the geographic scope (Canton-specific), and the intended use case. No critical information is missing for an agent to decide on invocation.
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 coverage is 100% with the 'limit' parameter fully documented ('Max rows per gap type (default 5)'). The description adds no additional parameter information, but since the schema carries the full burden, baseline 3 applies.
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 tool's purpose with a specific verb ('surface') and resource ('ecosystem gaps'), listing three concrete gap types: unanswered forum threads, unreplied mailing threads, and unlabeled Dev Fund proposals. This distinguishes it from siblings like get_funding_landscape and get_trending.
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 explicitly says 'Use to find where the ecosystem needs help' and names an alternative: 'For per-category funding levels use get_funding_landscape.' This gives clear when-to-use and an alternative, fully satisfying the criteria.
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.