get_packFree discovery. Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack. Call this before querying a new pack so you can see time shape, coverage limits, and the paste-ready first query.
| Name | Required | Description | Default |
|---|---|---|---|
| pack_id | Yes | Pack identifier from get_catalog. Newly catalog-admitted packs require no MCP schema change. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds context by calling it 'Free discovery' and explaining it returns coverage limits and time shape, which hints at the nature of the data and potential limitations. It doesn't reveal side effects (none expected) and aligns with the read-only annotation.
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, both purposeful: the first enumerates the return contents, the second gives an actionable usage directive. No wasted words, and the purpose is front-loaded.
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 simple read-only get tool with no output schema, the description covers what it returns, when to call it, and its purpose. It doesn't mention error handling or invalid pack_id behavior, but those are minor for a tool of this simplicity. Overall, it provides enough information for an agent to call it correctly.
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 pack_id described as 'Pack identifier from get_catalog.' The description adds no new parameter semantics beyond what the schema provides; it only refers to 'new pack' generally. Given the high coverage, baseline 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 states a specific verb ('Returns') and a specific resource ('detailed metadata... for one pack'), and distinguishes itself from sibling tools like get_catalog by focusing on a single pack rather than a list. The phrase 'one pack' clearly differentiates it from a catalog listing.
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?
Explicitly instructs to 'Call this before querying a new pack' and explains why (to see time shape, coverage limits, and the paste-ready first query). While it doesn't name alternative tools like get_catalog for comparison, the directive is clear and contextually appropriate for this discovery tool.
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.
Most tools are clearly distinct, but get_disaster_link_chain and get_disaster_links_for_event both operate on an exact event id and return related events, which could cause confusion. Descriptions clarify chain vs. links, but the boundary is subtle.
All tools follow a consistent get_<resource> or search_<resource> pattern, using snake_case throughout. The naming is predictable and makes it easy to infer function from the tool name.
With 8 tools, the server covers discovery, query, live data, and cross-disaster linking without being bloated. Each tool serves a clear purpose, making the count appropriate for a specialized volcanic activity data server.
The surface covers core volcanic activity queries, live events, pack discovery, and related-disaster exploration. Minor gaps exist such as no direct tool for fetching a single event by ID, but the existing tools can generally work around that.