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Qase MCP Server

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by qase-tms

qql_help

Read-onlyIdempotent

Look up QQL reference sections before writing queries to learn syntax, entity fields, operators, functions, or enum values. Read the relevant topic to avoid query errors from invalid attributes.

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. Dates show when Glama detected each change.

  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

A4.2/5.0
Behavior4/5

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

Annotations already establish readOnly/idempotent/non-destructive, and the description adds value beyond them: no API call, static text, ~2ms latency, and free invocations. It also discloses the failure mode it guards against (rejected queries from nonexistent attributes) and that field names diverge from write tools. No contradiction with annotations; only the output format of the returned reference text is left unspecified.

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

Conciseness4/5

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

Every sentence earns its place: purpose, critical topics, rationale, session cadence, and cost. The inline topic list partially duplicates the schema enum, and the most actionable instruction ('once per session') sits mid-description rather than front-loaded, but the density of useful information justifies the length.

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?

For a single-parameter, static-reference tool with fully documented enum values and strong annotations, the description covers everything essential: when to call, which topics exist, which matter most, why, and cost. The only gap is the unspecified shape of the returned reference text, which is minor given the low complexity and absence of an output schema.

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% with rich per-enum-value descriptions, so the baseline is 3. The description adds meaning by highlighting which topics matter most (entities, enumValues) and why — field names differ from write tools and unknown attributes cause rejection rather than being ignored. This elevates understanding beyond what the schema alone provides.

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 states a specific action — read the QQL reference — and enumerates the eight available topics, making the tool's function clear. It distinguishes itself from the qql_search sibling by framing this as the prerequisite reference lookup. Slight deduction: the phrasing is advisory ('Read the QQL reference before writing a query') rather than declarative about what the tool returns, so an agent must infer that the deliverable is the reference text for the requested topic.

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?

Explicitly names the alternative (qql_search), prescribes cadence ('once per session before the first qql_search'), and discourages the anti-pattern ('rather than guessing and retrying'). It also justifies the rule by disclosing the consequence — QQL rejects queries naming nonexistent attributes instead of ignoring them, and field names differ from the write tools. Nothing about when or when not to call is left to inference.

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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