dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the The Water Hub dataset. Call this first to learn the schema.
| 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?
With no annotations, the description carries full responsibility for disclosing behavior. It explains the returned contents (columns, numeric indicators, row count, provenance banner), but doesn't specify whether there are side effects, authentication requirements, or any output format details. It's transparent enough for a no-argument read-oriented tool, but not exhaustive.
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 sentence that front-loads the key output details and ends with a clear call-to-action. It is concise, though the phrasing 'of the The Water Hub dataset' is grammatically awkward.
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 zero-parameter schema-introspection tool with no output schema, the description adequately conveys what the agent will receive and when to call it. It could be more explicit about differentiation from dataset_provenance, but it's sufficient for a first-step discovery call.
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 fully covers this by declaring an empty properties object. The description doesn't need to explain parameter semantics, so the baseline 4 applies.
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 identifies the tool's output: columns, numeric flags, row count, and provenance banner. The directive 'Call this first to learn the schema' gives the tool a clear purpose, though it doesn't explicitly distinguish it from sibling tools like dataset_provenance.
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 'Call this first to learn the schema' provides clear context for when to use it, positioning it as an initial schema-discovery step. It doesn't explicitly state when not to use it or mention alternatives, but the ordering guidance is useful and specific.
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 targets a distinct operation on the dataset or enquiry. dataset_search, dataset_row, dataset_compare, and dataset_top have clear differences in matching logic and output. Enquiry tools are clearly separate. No two tools appear to do the same thing.
All dataset tools follow a dataset_* pattern, and enquiry tools follow enquiry_* except submit_enquiry which inverts the order. This is a minor deviation but the overall naming is predictable and clear.
10 tools is well-scoped for the domain, covering both data querying and enquiry submission without bloat.
The dataset query surface covers schema, metadata, row lookup, search, stats, and top/bottom. Enquiry covers description, fields, and submission. Minor gaps like a 'list all rows' or enquiry status check exist but are not critical.