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by com.homecoverhq

Submit an ENQUIRY to human providers (two steps; not a purchase)

submit_enquiry

Submits an enquiry to HomeCover HQ — NOT a purchase, NOT a guaranteed quote. Step 1: call with the answers (keyed by field key from enquiry_fields) and consent=true; it validates and returns a summary, the consent line and a confirmation token — show the person the summary and the consent line. Step 2: only if the person agrees, call again with the same answers, consent=true and the confirmation token; the enquiry is then submitted, and the person receives an email with a link they must click before any provider sees it. Consent means the person has read and agreed to: "By submitting you agree HomeCover HQ shares your details with licensed insurance agents who may contact you."

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answersYesthe person's answers, keyed by field key
consentYestrue only when the person has agreed to: By submitting you agree HomeCover HQ shares your details with licensed insurance agents who may contact you.
confirmationNothe confirmation token from step 1, after the person has approved the summary

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden, and it does so thoroughly. It discloses that step 1 is only validation, step 2 is the actual submission, and that providers only see the enquiry after the person clicks an email link. It also spells out the exact consent line and legal implication, which is critical behavioral context for an agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the most important constraints and then walks through the two steps in order. Every sentence carries necessary operational information; none is filler or redundant with the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description explains what step 1 returns (summary, consent line, confirmation token) and what step 2 triggers (submission and email link). It accounts for the two-step workflow, consent handling, and the human-provider context, so an agent has enough to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds meaningful semantics beyond the schema: it explains that answers are keyed by enquiry_fields keys, that consent requires the person to have agreed to the exact statement, and that confirmation is the token returned by step 1. This enriches the otherwise bare parameter definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource ('Submits an enquiry to HomeCover HQ') and immediately distinguishes the tool from a purchase or quote. It also clearly defines the two-step nature, so an agent understands exactly what operation this tool performs and what it does not.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when and how to use the tool: step 1 requires answers and consent and returns a token; step 2 only happens after the person approves, using the same answers plus the token. It also says the tool is not for purchases and references enquiry_fields for answer keys, which orients the agent toward related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness5/5

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.

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