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GoodLeads

by club.goodleads
OverviewDiscussionsAdmin

Interpret a list request

interpret_list
Read-onlyIdempotent

Start here: the buyer's own words become a list we can count, price and sell.

Give it what the buyer would type ("cleaning companies in Texas", "denver
plumbers formed last 30 days with a phone", "NAICS 238220") and you get
back a list shape in the one filter contract — `states`, `filters`, `sort`,
`lane`, `cap` — with a one-sentence `readback` to show the buyer, `assumed`
(every default and substitution, named), `unresolved` (the words it could
not place) and up to three `alternatives`. It is the same interpreter
behind the buy page's search box, so a person and an agent get the same
list from the same words. It never answers in prose, never asks a question
back, never looks a person up, and never emits a predicate on a masked
field (`contact_name`, `email_primary`, `phone_primary`).

When the buyer asked a question or raised an objection instead ("where does
this come from", "is it legal to call", "how fresh"), the response also
carries `answer` (`{family, headline, body, next_step, facts}`) — our
answer, in our words. Relay it to the buyer verbatim.

When the ask pulls two ways — the newest records AND a phone to call —
`alternatives` come back live-quoted (`quote: {records, total_cents,
unit_cents}`, a `why`, one `recommended`): call today · mail first with
phones verified on order · a standing order. The close is two questions:
present your human the quoted choice, then hand over the payment link for
the one chosen — per record, no minimums, so a small first order is the
normal first step.

Next: hand the shape to `quote_list` for the count and the price, then to
`checkout_list` to buy it.

Args:
    text: What the buyer typed, in their own words.
    state: Optional two-letter state hint (live states: CO, CT, FL, NY, TX, VA).
    current: Optional current shape `{states, filters, lane, cap}` — the
        answer merges into it instead of starting over.

Returns:
    `{states, filters, sort, lane, cap, readback, assumed, unresolved,
    alternatives, used_model}` — always a shape, never a 500.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
stateNo
currentNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations include readOnlyHint, idempotentHint, and destructiveHint false, so safety is covered. The description adds critical behavioral details that annotations don't: it never answers in prose, never asks a question back, never looks a person up, and never emits predicates on masked fields (with specific field names). It also states it always returns a shape, never a 500, and describes the `answer` structure for question/objection handling.

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

Conciseness3/5

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

The description is comprehensive but somewhat lengthycars, with 5 paragraphs. The most critical purpose and usage are front-loaded in the first paragraphcars, and the 'Next' step is at the end. However, some details like the `answer` structure could be condensed; the level of detail is justifiable given the complexity, but it borders on being verbose. Still, every sentence serves a purpose.

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?

Given the tool's complexity and the rich output schema, the description is thorough: it explains the return shape fields, the `answer` sub-structure, and the `alternatives` with quotes. It also covers the behavioral constraints, examples, and next steps. The presence of an output schema reduces the need to describe return values in detail, but the description goes beyond, covering edge cases like questions and ambiguous asks.

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 0%, so the description must explain parameters. It does: `text` is described as the buyer's typed words with examples; `state` is described as an optional two-letter hint; `current` is described as the existing shape to merge into. This goes beyond the schema's bare property names, giving meaning and use context. Misses minor details like where `state` comes from, but enough for an agent to understand.

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 clearly states the tool's purpose: converting a buyer's natural language request into a structured list shape. It uses specific verbs ('interpret', 'convert', 'return') and names the resource ('the buyer's own words'). It distinguishes itself from siblings by explicitly naming next steps (quote_list, checkout_list) and contrasting with other tools like browse_leads and find_lead_by_glid.

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 provides explicit when-to-use guidance: 'Start here' for list requests, with multiple concrete examples of what the buyer might type. It also explains when the tool handles questions/objections, and when the tool should be used for ambiguous asks with alternatives. It also names alternatives: 'Next: hand the shape to quote_list' implies use this before those, and mentions what it never does (never asks questions, never looks up a person), clarifying 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.

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