discovery

Get a query-shaped answer

get_answer

Read-only

Returns one authored /answers page as structured content: question, markdown answer body (short answer, problem frame, evidence, grounded sources), and citations with libraryId or url. Known slugs (33): measure-employee-performance, survey-validity-check, people-analytics-program-maturity, people-analytics-vs-hr-reporting, engagement-survey-vs-listening-diagnostic, people-analytics-tools-compare, beyond-annual-engagement-survey, replace-gut-feel-talent-decisions, ….

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": {
      "slug": "<slug>"
    },
    "name": "get_answer"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes	Answer slug from the query map (e.g. survey-validity-check).

Output Schema

Name	Required	Description	Default
`slug`	Yes
`title`	Yes
`answer`	Yes
`question`	Yes
`citations`	Yes
`updatedAt`	Yes
`canonicalUrl`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 8, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds that exactly one answer page is returned and what its structure is, but it does not disclose error behavior, edge cases, or other hidden traits beyond the annotation baseline.

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 front-loaded with the operation and return contents, followed by a compact list of slug examples. Every sentence earns its place, and there is no vague filler or repetition of the tool 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?

With an output schema present and one fully documented parameter, the description is mostly sufficient. However, only 8 of the 33 known slugs are listed and there are no enums, so the agent cannot reliably determine the complete set of valid slug values.

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 fully documents the single 'slug' parameter with 100% coverage and an example. The description adds the total count of known slugs and eight concrete examples, which materially improves an agent's ability to pick a valid value beyond what the schema provides.

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 specific verb and resource: 'Returns one authored /answers page as structured content,' and enumerates exactly what that content includes. This clearly distinguishes the tool from sibling content-getters by naming the unique '/answers page' resource.

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?

The 'Known slugs' list implies when the tool is applicable, and the resource type suggests it is for retrieving pre-authored answer pages. However, the description does not explicitly say when to prefer this tool over a sibling, nor does it mention any exclusions or alternative tools.

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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