Get Catalog
get_catalogFree discovery. Returns the list of live agent-ready data packs available on DaedalMap.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_catalogFree discovery. Returns the list of live agent-ready data packs available on DaedalMap.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 the description adds 'Free discovery' to convey that no cost or auth is needed. It does not detail return format or error behavior, but for a parameterless read-only operation, the provided context is adequate.
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: 'Free discovery. Returns the list of live agent-ready data packs available on DaedalMap.' Every phrase adds value, with the purpose front-loaded and no fluff.
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 parameterless read-only tool, the description sufficiently explains what it returns and adds platform context. It does not elaborate on the structure of 'data packs' or output format, but given the lack of an output schema and the tool's simplicity, the description is nearly complete.
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?
The tool has zero parameters, and the schema covers 100% of parameters vacuously. Per the rubric, a baseline of 4 is appropriate when there are no parameters, and the description adds no unnecessary detail about parameters.
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 function: 'Returns the list of live agent-ready data packs available on DaedalMap.' This specifies the verb (Returns), the resource (list of data packs), and the platform, distinguishing it from siblings like get_pack and query_dataset.
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 phrase 'Free discovery' and the description of returning the catalog make it clear this is the tool for listing available data packs, not for querying or searching. However, it does not explicitly mention alternatives or exclusions, so it stops short of a full 5.
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 clearly distinct purpose: catalog lists packs, get_pack returns pack metadata, get_tool_help explains tool usage, query_dataset executes data queries, and search_disaster_links discovers cross-disaster relationships. There is no meaningful overlap or ambiguity between them.
All tool names follow a consistent lowercase snake_case verb_noun pattern: get_catalog, get_pack, get_tool_help, query_dataset, search_disaster_links. The verb differences reflect different action types, but the structural pattern is uniform.
Five tools is well-scoped for a data-access MCP server. The set covers discovery, metadata retrieval, tool guidance, querying, and relationship search without unnecessary bulk or redundancy.
The tool surface covers the full discovery-to-query workflow: find packs, inspect pack metadata, get tool help, query datasets, and search disaster link families. A minor gap is the lack of dataset-level schema or status tools, but these are partly covered by get_pack metadata and are not critical for the server's read-oriented purpose.