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, so the description's focus on 'Returns the list' does not contradict that or add much beyond the annotation. The phrase 'live agent-ready data packs' adds context about what data is available, but does not disclose additional behavioral traits such as pagination, rate limits, or response structure.
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 a single, clear sentence that communicates purpose and key context ('Free discovery', 'live agent-ready data packs') without wasted words. It is appropriately sized for the tool's simplicity.
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 listing tool, the description adequately states what it returns and the domain (DaedalMap). There is no output schema, but the return type ('list') is stated. Combined with the sibling context, this is sufficient for an agent to select and invoke the tool 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?
The tool has zero parameters, so the description does not need to explain parameter semantics. The schema already documents this fully (schema description coverage 100%), and the baseline for zero-parameter tools is 4.
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 uses a specific verb ('Returns') and resource ('list of live agent-ready data packs available on DaedalMap'), clearly distinguishing it from siblings like get_pack and query_dataset. The 'Free discovery' phrase clarifies it's a zero-cost listing operation.
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 implies use for discovering available data packs ('Free discovery'), but does not explicitly state when to use this tool versus alternatives like get_pack or query_dataset. It provides some contextual hint but lacks explicit when-to-use or when-not-to-use guidance.
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: get_catalog discovers available packs, get_pack provides metadata for a specific pack, get_tool_help gives usage guidance for a tool, and query_dataset executes queries. There is no overlap or ambiguity in their roles.
Three tools follow a 'get_*' pattern (get_catalog, get_pack, get_tool_help), but query_dataset breaks the pattern with a different verb. The objects vary (catalog, pack, tool_help, dataset) without a strict verb_noun structure, but the names remain understandable and readable.
With 4 tools, the server is well-scoped for a data access/query facade. Each tool covers a necessary step in the workflow (discover, inspect, get help, query) without bloat, fitting comfortably in the ideal 3-15 range.
The tool surface covers the full user journey for a data query server: discovery (get_catalog), metadata inspection (get_pack), tool guidance (get_tool_help), and actual querying (query_dataset). Minor gaps exist, such as no explicit way to list all available tools without using tools/list, but that is an MCP protocol concern rather than a domain coverage gap.