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 reinforces this with 'Free discovery' while adding context about what the returned metadata includes and the recommended pre-query behavior. No contradictions; it adds useful behavioral context beyond the 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 with no filler: the first states what is returned, the second states when to call it. The most decision-relevant information (discovery, metadata, first-query examples) 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 read-only, single-parameter lookup tool with no output schema, the description covers what it returns, why it exists, and when to call it. The only missing piece (pack listing) is handled by the get_catalog sibling referenced in the schema.
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 description coverage is 100%, so the schema fully documents pack_id as a string identifier from get_catalog. The description adds no new parameter-level detail, which is acceptable under the baseline-3 rule for complete schema coverage.
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 and resource ('Returns detailed metadata... for one pack') and enumerates the kinds of information returned (coverage, freshness, canonical tool guidance, first-query examples). It distinguishes this tool from siblings: get_catalog supplies the pack listing, while get_pack is the per-pack discovery step.
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 gives explicit when-to-use guidance: 'Call this before querying a new pack so you can see time shape, coverage limits, and the paste-ready first query.' It does not explicitly name exclusion conditions or contrast with get_catalog/get_tool_help, but the intended call sequence is clear.
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.
Each tool has a clear, distinct role: catalog discovery, pack metadata, tool help, data querying, and link searching. There is no overlap between any two tools; even get_catalog and get_pack differ as list vs. details.
All tool names follow a consistent verb_noun snake_case pattern: get_catalog, get_pack, get_tool_help, query_dataset, search_disaster_links. The verbs and nouns are consistently ordered, making the API predictable.
Five tools is well-scoped for a data access facade. Each tool earns its place: discovery (catalog, pack, help), execution (query), and specialized search (links). No redundancy or bloat.
The server covers the full lifecycle of data exploration: discover available packs, inspect pack details, understand tool usage, execute queries, and search for relationships. There are no apparent dead ends—an agent can go from discovery to successful query.