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QQL syntax help

qql_help
Read-onlyIdempotent

Get QQL reference on syntax, entities, operators, functions, and enum values to write valid queries and avoid rejection.

Instructions

Read the QQL reference before writing a query. Pass a topic: overview, syntax, entities, operators, functions, examples, aggregation, or enumValues. entities lists the fields each entity actually exposes, and enumValues gives the accepted values for status, priority, severity and the rest — both matter, because QQL rejects a query naming an attribute that does not exist on the entity rather than ignoring it, and the field names differ from those in the write tools. Read this once per session before the first qql_search rather than guessing and retrying. Cost: no API call, static text, about 2ms. Free to call, and cheaper than one rejected query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesWhich section to return (required — one section per call): - overview: what QQL is, overall query structure, subscription requirement - syntax: structure, ordering, custom fields, case-sensitivity, boolean and date fields - entities: the fields available on each entity — field names are NOT uniform across entities, so read this before writing a query against an unfamiliar one - operators: comparison, matching, set, null, and logical operators - functions: currentUser, activeUsers, and the now/startOf*/endOf* date functions - examples: ready-made queries for common questions - aggregation: SELECT (COUNT/MIN/MAX/AVG/SUM/FIRST/LAST), GROUP BY, HAVING — use this to count or summarise instead of paging through rows - enumValues: the valid values for priority, severity, and the per-entity status fields

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv2.1.1
    • changedInput schema / properties / topic / description
      Previous value: -"Specific help topic, or omit for general overview"New value: +"Which section to return (required — one section per call):\n- overview: what QQL is, overall query structure, subscription requirement\n- syntax: structure, ordering, custom fields, case-sensitivity, boolean and date fields\n- entities: the fields available on each entity — field names are NOT uniform across entities, so read this before writing a query against an unfamiliar one\n- operators: comparison, matching, set, null, and logical operators\n- functions: currentUser, activeUsers, and the now/startOf*/endOf* date functions\n- examples: ready-made queries for common questions\n- aggregation: SELECT (COUNT/MIN/MAX/AVG/SUM/FIRST/LAST), GROUP BY, HAVING — use this to count or summarise instead of paging through rows\n- enumValues: the valid values for priority, severity, and the per-entity status fields"
    • changedInput schema / properties / topic / enum
      Previous value: -[
-  "syntax",
-  "entities",
-  "operators",
-  "functions",
-  "examples"
-]New value: +[
+  "overview",
+  "syntax",
+  "entities",
+  "operators",
+  "functions",
+  "examples",
+  "aggregation",
+  "enumValues"
+]
    • addedInput schema / required
      Added value: +[
+  "topic"
+]
  2. First observedv1.1.7

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses that this makes no API call, returns static text, costs about 2ms, and is free. It also explains QQL's rejection behavior for nonexistent attributes, which is important context for the agent's subsequent query-writing behavior.

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 dense but well-structured, front-loading the crucial instruction, then listing topics, key caveats, and cost. Every sentence adds practical value, and the repeated topic list serves as an orienting summary rather than pure redundancy.

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?

For a one-parameter static-help tool with strong readOnly/idempotent annotations, the description fully covers when to call, how to call, why it matters, what it costs, and what behavior to expect. No output schema is needed because the tool returns static reference text, which the description clearly states.

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?

The schema already documents each topic in detail, so the baseline is 3. The description adds meaning by highlighting entities and enumValues as critical topics and warning that field names differ from those in write tools, which goes beyond the schema's enumerated list.

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 states a clear imperative—'Read the QQL reference before writing a query'—and defines the tool as the QQL syntax reference with a bounded topic set. It distinguishes itself from qql_search by positioning itself as the pre-query reference step rather than the query execution tool.

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?

It explicitly says to call this tool before the first qql_search, once per session, and 'rather than guessing and retrying.' It also names the sibling qql_search and provides a cost rationale for using help first, giving clear when-to-use guidance.

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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