get_answerOne AI answer in full, with the brands it mentions, its recommendations, ranked list, outcome, and citations. Use it to read an answer_id from list_answers; to scan many answers use list_answers.
| Name | Required | Description | Default |
|---|---|---|---|
| answer_id | Yes | Answer ID from list_answers. |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| generated_at | Yes | RFC 3339 time the response was made. | |
| schema_version | Yes |
Changes observed during successful MCP inspections.
Output schema / properties / data / properties / citations / items / properties / position / descriptionAdded value: +"Character offset of the citation in the answer text; null unless the provider gave spans."Input schema / additionalPropertiesAdded value: +falseInput schema / properties / answer_id / minLengthAdded value: +1Output schema / (root)Previous value: -nullNew value: +{
+ "additionalProperties": false,
+ "properties": {
+ "data": {
+ "additionalProperties": false,
+ "properties": {
+ "brands_mentioned": {
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "brand_id": {
+ "type": "string"
+ },
+ "context": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "position": {
+ "description": "Order among mentioned brands, 1 = first.",
+ "type": "integer"
+ },
+ "sentiment": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "brand_id",
+ "name",
+ "position",
+ "sentiment",
+ "context"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "captured_at": {
+ "type": "string"
+ },
+ "citations": {
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "domain": {
+ "type": "string"
+ },
+ "ownership": {
+ "description": "own, competitor, or third_party.",
+ "type": "string"
+ },
+ "position": {
+ "type": [
+ "null",
+ "integer"
+ ]
+ },
+ "title": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "url",
+ "domain",
+ "title",
+ "ownership",
+ "position"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "id": {
+ "type": "string"
+ },
+ "model": {
+ "description": "Model family key.",
+ "type": "string"
+ },
+ "model_name": {
+ "type": "string"
+ },
+ "model_version": {
+ "description": "The exact model that answered.",
+ "type": "string"
+ },
+ "outcome": {
+ "additionalProperties": false,
+ "description": "null until classified.",
+ "properties": {
+ "labels": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "outcome": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "outcome",
+ "labels"
+ ],
+ "type": [
+ "null",
+ "object"
+ ]
+ },
+ "prompt_id": {
+ "type": "string"
+ },
+ "ranked_list": {
+ "description": "The answer's own numbered list, when it has one.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "brand_id": {
+ "description": "Set when the entry is a tracked brand.",
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "position": {
+ "type": "integer"
+ }
+ },
+ "required": [
+ "position",
+ "name",
+ "brand_id"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "recommendations": {
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "brand_id": {
+ "type": "string"
+ },
+ "context": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "rank": {
+ "description": "Position in the answer's ranking; null when unranked.",
+ "type": [
+ "null",
+ "integer"
+ ]
+ },
+ "status": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "brand_id",
+ "name",
+ "status",
+ "rank",
+ "context"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "text": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "id",
+ "prompt_id",
+ "model",
+ "model_name",
+ "model_version",
+ "captured_at",
+ "text",
+ "brands_mentioned",
+ "recommendations",
+ "ranked_list",
+ "outcome",
+ "citations"
+ ],
+ "type": "object"
+ },
+ "generated_at": {
+ "description": "RFC 3339 time the response was made.",
+ "type": "string"
+ },
+ "schema_version": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "schema_version",
+ "generated_at",
+ "data"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), so the bar is lower. The description adds what the response contains, but since an output schema exists that content is largely duplicated rather than additive. No auth, rate-limit, or error behavior is disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences, front-loaded with what is returned and followed by the routing rule. No filler and nothing that fails to earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema handling return-value structure and annotations handling the safety profile, the description needs only to state identity and routing, which it does completely. Nothing an agent needs to call it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is a single parameter with 100% schema description coverage, so the schema already documents answer_id fully. The description reinforces its provenance ('an answer_id from list_answers'), which adds a little origin context but no format or syntax detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (get) and resource (one AI answer) and enumerates the payload contents (brands, recommendations, ranked list, outcome, citations). This clearly distinguishes it from the sibling list_answers, which is named explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit routing: use this to read a single answer_id obtained from list_answers, and use list_answers instead to scan many answers. Both the when-to-use and the alternative are stated with no inference required.
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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