enquiry_fieldsEvery field of the HomeCover HQ enquiry: key, label, type, whether required, help text and the allowed options where there are any. Pass answers to submit_enquiry keyed by field key.
| 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 provided, the description carries the burden of behavioral disclosure. It explains what the tool returns (key, label, type, required flag, help text, options) and implies a read-only metadata lookup, but it does not explicitly state that there are no side effects or describe any other behavioral constraints such as authentication or data freshness.
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 exceptionally compact: two sentences, front-loaded with the resource and attribute list, followed by a single operational pointer. No filler, no redundant restatement of the title.
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 tool with no output schema, the description covers the returned field attributes and the relationship to submit_enquiry. It does not explicitly state whether the result is an array or map, but this is not essential for invoking 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 input schema has zero properties, so there are no parameters to document; baseline is 4. The description adds valuable downstream meaning by clarifying that the returned field keys should be used as keys when calling submit_enquiry, which helps an agent apply the output correctly.
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 resource: 'every field of the HomeCover HQ enquiry' and enumerates the returned attributes. It distinguishes its purpose from submit_enquiry by noting that answers should be keyed by these field keys, though it does not explicitly contrast with enquiry_describe and lacks a direct verb like 'list' or 'get'.
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 the tool is a prerequisite for submit_enquiry: 'Pass answers to submit_enquiry keyed by field key'. This gives useful context for when to use it, but it does not explicitly state when to choose this tool over alternatives or when not to use it.
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.
Most tools have clearly distinct purposes: schema, provenance, exact row lookup, substring search, comparison, stats, top values, and enquiry steps are all separate. The only mild ambiguity is between dataset_row, dataset_search, and dataset_compare, but their descriptions clarify exact matching, substring matching, and ordered value comparison respectively.
The dataset_* prefix and enquiry_* prefix create a clear grouping. Within each group the pattern is mostly consistent, though some names are noun-based (dataset_columns, dataset_provenance) while others are verb-based (dataset_search, dataset_compare), and submit_enquiry reverses the prefix order.
Ten tools is a well-scoped set for this domain: seven query tools cover the dataset surface and three cover the enquiry flow. Each tool has a distinct job and none feel redundant.
The dataset side covers schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking, which covers the full range of likely questions. The enquiry side handles explaining the process, listing fields, and submitting with a two-step confirmation, leaving no obvious dead ends.