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openapi_v2_keyword_trend

Get keyword trend

Agent-only batch response contract. Return weekly time-series trend metrics for one or more keywords, including snapshot data between dateFrom and dateTo; single-keyword requests also return data.context + data.items[].series[]. The date range cannot exceed 93 days; split longer history into multiple requests.

Responses:

200: Successful Response (Success Response) Content-Type: application/json

Example Response:

{
  "success": true,
  "meta": {
    "requestId": "Requestid",
    "timestamp": "Timestamp"
  }
}

Output Schema:

{
  "properties": {
    "success": {
      "type": "boolean",
      "title": "Success",
      "description": "Whether the request was successful",
      "default": true
    },
    "data": {
      "description": "Response data payload"
    },
    "error": {
      "description": "Error details if request failed"
    },
    "meta": {
      "description": "Metadata for API responses.\n\nCredit fields follow the ADR-0003 parallel-fields strategy (Option 3):\n- `credits_remaining` / `credits_consumed` (int): legacy fields, rounded\n  to whole credits, kept for zero-breaking-change to existing SDK clients.\n- `credits_remaining_exact` / `credits_consumed_exact` (float): new\n  precision-aware fields for clients that opt in to decimal credits.\n\nSee ADR-0003 decision 5 and the \u00a78 deprecation timeline.\n\nTODO(2026-11, ADR-0003 \u00a78 +6mo): mark `credits_remaining` /\n`credits_consumed` as `deprecated=True` in their Field() definitions\nand announce in customer changelog.\nTODO(2027-05, ADR-0003 \u00a78 +12mo): remove the legacy int fields via a\nmajor-version bump of the OpenAPI surface.",
      "properties": {
        "requestId": {
          "type": "string",
          "title": "Requestid",
          "description": "Unique request identifier"
        },
        "timestamp": {
          "type": "string",
          "title": "Timestamp",
          "description": "Response timestamp in ISO 8601 format"
        },
        "total": {
          "title": "Total",
          "description": "Total number of records"
        },
        "page": {
          "title": "Page",
          "description": "Current page number"
        },
        "pageSize": {
          "title": "Pagesize",
          "description": "Number of records per page"
        },
        "totalPages": {
          "title": "Totalpages",
          "description": "Total number of pages"
        },
        "creditsRemaining": {
          "title": "Creditsremaining",
          "description": "Remaining API credits (rounded to whole credits; see creditsRemainingExact for precise value)"
        },
        "creditsConsumed": {
          "title": "Creditsconsumed",
          "description": "Credits consumed by this request (rounded; see creditsConsumedExact for precise value)"
        },
        "creditsRemainingExact": {
          "title": "Creditsremainingexact",
          "description": "Remaining API credits, precise to 1 decimal place"
        },
        "creditsConsumedExact": {
          "title": "Creditsconsumedexact",
          "description": "Credits consumed by this request, precise to 1 decimal place"
        },
        "tokensUsage": {
          "description": "Provider token-usage block \u2014 populated on terminal video polls only, null on every non-video endpoint. See TokensUsage for its fields."
        }
      },
      "type": "object",
      "required": [
        "requestId",
        "timestamp"
      ],
      "title": "ResponseMeta"
    }
  },
  "type": "object",
  "required": [
    "meta"
  ],
  "title": "OpenApiResponse[KeywordTrendData]",
  "examples": []
}

422: Validation Error Content-Type: application/json

Example Response:

{
  "detail": [
    {
      "loc": [],
      "msg": "Message",
      "type": "Error Type",
      "ctx": {}
    }
  ]
}

Output Schema:

{
  "properties": {
    "detail": {
      "items": {
        "properties": {
          "loc": {
            "items": {},
            "type": "array",
            "title": "Location"
          },
          "msg": {
            "type": "string",
            "title": "Message"
          },
          "type": {
            "type": "string",
            "title": "Error Type"
          },
          "input": {
            "title": "Input"
          },
          "ctx": {
            "type": "object",
            "title": "Context"
          }
        },
        "type": "object",
        "required": [
          "loc",
          "msg",
          "type"
        ],
        "title": "ValidationError"
      },
      "type": "array",
      "title": "Detail"
    }
  },
  "type": "object",
  "title": "HTTPValidationError"
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateToYesTrend end date in YYYY-MM-DD format. The response contains weekly snapshots within the requested date range.
keywordNoKeyword to look up.
dateFromYesTrend start date in YYYY-MM-DD format. The response contains weekly snapshots within the requested date range.
keywordsNoKeywords to look up in batch, up to 20.
marketplaceNoAmazon marketplace code. Only 'US' is currently supported.US

TDQS

A3.5/5.0
Behavior4/5

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

Despite having no annotations, the description discloses meaningful runtime behavior: weekly snapshots, single-keyword extra fields, and the 93-day hard limit. It also includes response schemas so the agent can anticipate success and validation shapes. It doesn't explicitly state read-only/no-side-effect or rate limits, but 'Get' and the content make that reasonably clear.

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

Conciseness2/5

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

The key behavior is front-loaded, but the description is dominated by a large embedded JSON output schema, including an extensive meta block with ADR references and deprecation TODOs that an agent does not need to select or invoke the tool. The useful part is short; the overall payload is bloated.

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?

The description covers the response envelope, date constraints, and single-keyword return shape, and the schema documents the parameters. However, it never states that at least one of `keyword`/`keywords` is required, what happens if both are supplied, or what the batch response data shape looks like, so an agent could construct an invalid or surprising request.

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 input schema already covers all 5 parameters, so the baseline is 3; the description adds the 93-day date-range rule and notes that single-keyword requests return additional data.context/series fields, which helps connect date/keyword parameters to behavior. It doesn't clarify whether `keyword` and `keywords` are mutually exclusive or which one is required for a valid call.

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 opens with a clear verb+resource ('Get keyword trend') and elaborates with a concrete return contract: weekly time-series trend metrics, snapshot data between dateFrom/dateTo, and single-keyword response details. It does not explicitly distinguish itself from sibling trend/profile tools, but the weekly-series wording reduces ambiguity.

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?

It gives operational guidance (93-day max, split longer ranges, batch vs single-keyword response contract) but never says when to prefer this over siblings such as openapi_v2_keyword_trend_profile or what alternatives exist. The usage context is implied rather than explicit.

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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TDQS

B3.2/5.0
Disambiguation2/5

Several tools have unclear boundaries, most notably openapi_v2_competitor_product_keywords and openapi_v2_product_traffic_terms, which have identical descriptions. The deprecated openapi_v2_realtime_product duplicates openapi_v3_realtime_product, and openapi_v2_image_embedding overlaps heavily with openapi_v2_fashion_image_embedding.

Naming Consistency2/5

Naming is inconsistent: a few tools follow a clean verb_noun pattern (create_video_asset, list_video_assets, poll_video_task), while the vast majority are prefixed with openapi_v2_/openapi_v3_ followed by nouns or mixed verbs. The route-style prefix is not a meaningful verb and creates an arbitrary split across the tool set.

Tool Count2/5

49 tools is far beyond the well-scoped range and spans multiple unrelated domains (Amazon product data, TikTok commerce, fashion vision, web scraping, video generation, billing). The server appears to be an entire REST API surface exposed wholesale rather than a curated set of capabilities.

Completeness3/5

Each domain is individually fairly complete (Amazon search/reviews/keywords/VoC, TikTok search, web tools, video generation), but there are notable gaps: video assets lack update/delete, video tasks lack cancel/list, and there is no singular 'get daily product by ASIN' alongside the realtime variants. The broad scope makes it hard to verify full lifecycle coverage across all domains.

Resources