DouxProcess Directory

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ask

Read-onlyIdempotent

Ask in plain words ('licensed electrician near 92101 who does EV chargers') and get a short answer built only from listings, plus the businesses it rests on, in the published order. Same rules as find_businesses: no ranking or recommendation language, coverage stated honestly.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question": "<question>"
    },
    "name": "ask"
  }
}

Input Schema

Name	Required	Description	Default
`where`	No	A place, if it is not in the question.
`question`	Yes	The person's question, in their words.

Output Schema

Name	Required	Description	Default
`note`	No
`text`	Yes
`label`	No
`intent`	No
`covered`	No
`ranking`	Yes
`has_more`	No
`more_url`	No
`followups`	No
`businesses`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 24, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is low, yet the description adds real behavioral context: answers are grounded only in listings, the underlying businesses are returned, results follow published order, and no recommendation language is permitted. It doesn't address latency or answer length limits, keeping it short of a 5.

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?

Two tight sentences with zero filler, leading with the example invocation and following with output shape and constraints. Every clause earns its place.

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

Completeness4/5

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

With an output schema present, the description needn't enumerate return fields, and it still characterizes the answer plus supporting businesses. A mutation-free read tool with full schema coverage and clear grounding rules is adequately described; only the ask-vs-find_businesses routing is under-specified.

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%, so the baseline is 3. The description adds a concrete worked example ('licensed electrician near 92101 who does EV chargers') that demonstrates how place can be embedded in the question, which ties directly to the optional 'where' parameter.

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

Purpose4/5

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

The description names a specific action (ask a plain-language question) and specifies the output: a short answer built only from listings, plus the businesses it rests on, in published order. It references find_businesses as the rule-sharing sibling, but does not sharply contrast what ask does that find_businesses doesn't, leaving differentiation partly implicit.

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

Usage Guidelines3/5

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

It says 'Same rules as find_businesses: no ranking or recommendation language, coverage stated honestly,' which conveys constraints but not a when-to-use decision. An agent must infer that ask is for synthesized answers while find_businesses is for raw result lists; no exclusion or explicit routing is given.

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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