get_dataset_infoReturn source, license, freshness, counts, and hosted aggregate metrics.
| Name | Required | Description | Default |
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No arguments | |||
| Name | Required | Description | Default |
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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, idempotentHint, and destructiveHint, covering the safety profile. The description adds the specific fields returned, which is useful but does not go beyond what annotations plus output schema would imply.
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?
A single, front-loaded sentence that names all key return contents. No wasted words.
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?
The tool is simple (no params, read-only, idempotent) and has an output schema, so the description does not need to explain return values. The description names the key data categories and is complete for this scope.
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 schema is empty and there is nothing to explain. Baseline for 0 params is 4, which 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 uses a specific verb 'Return' and names precise resource components (source, license, freshness, counts, hosted aggregate metrics). This clearly distinguishes it from sibling settlement-related tools.
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?
No explicit when-to-use or alternative guidance is provided. However, the description clearly implies it is for dataset metadata retrieval, and sibling names confirm it is separate from settlement operations, so usage context is indirectly 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 clearly distinct purpose: dataset metadata, single-record retrieval, batch retrieval, single search, and batch search. No overlapping boundaries or potential for misselection.
All tool names follow a consistent verb_noun snake_case pattern (get_dataset_info, get_settlement, get_settlements, search_settlements, search_settlements_batch). Verbs and noun plurality are used logically and consistently.
With exactly 5 tools, the server is well-scoped for its apparent purpose of settlement searching and retrieval. Each tool fills a necessary role without redundancy, making the count ideal.
The tool surface fully covers the read-only workflow: dataset-level info, targeted retrieval (single and batch), and search (single and batch). No obvious gaps for the domain, as update/create/delete operations are not implied by the server's purpose.