Skip to main content
Glama

Adh.Query.Start

adh.query.start

Start a stored ADH analysis query for a customer across a date range and return an asynchronous job reference; poll the jobs endpoint to retrieve results.

Instructions

Start a stored ADH analysis query over a date range; returns an operation/job ref (async, privacy-checked run — poll adh.jobs.get for the result).

Input Schema

NameRequiredDescriptionDefault
end_dateYes
query_idYes
start_dateYes
customer_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations at all, the description carries the full burden and does disclose key traits: the run is asynchronous, it is privacy-checked, and it returns a job reference rather than results. It does not cover permissions required, what happens on a privacy failure, or whether a repeated call spawns a second job — meaningful gaps for a mutation-style launcher.

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?

A single front-loaded sentence: purpose first, then return value, then the async/polling note. No filler, and the parenthetical is doing genuine work rather than restating the name.

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

Completeness3/5

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

Return values need not be explained since an output schema exists, and the async lifecycle is covered. However, for a four-parameter launch tool with zero schema descriptions and no annotations, the total absence of parameter meaning and failure-mode context leaves the definition only minimally sufficient.

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

Parameters2/5

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

Schema description coverage is 0% with four required parameters, so the description must compensate — but it only alludes to the date range (start_date/end_date). customer_id and query_id are completely unexplained in both the schema and the description, leaving an agent to guess what a 'stored query' id refers to.

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?

States a specific verb ('Start') on a specific resource ('stored ADH analysis query') with a scoped input ('over a date range') and a declared return ('operation/job ref'). It also names the follow-up sibling adh.jobs.get, so an agent can distinguish launching a run from creating a query definition (adh.query.create) or fetching results.

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

Usage Guidelines4/5

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

The description embeds a clear workflow instruction: the call is async and results must be retrieved via adh.jobs.get. That is real routing guidance, but it gives no exclusion criteria (e.g., when to use adh.query.create vs. this tool, or behavior if a query is already running).

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

Deploy Server

Other Tools