AthenaHQ

get_attribute_time_series

Read-only

Get the daily trend for ONE brand trait (attribute), brand and competitors side by side — how mentions of a keyword like 'Affordable' moved over time. Requires an attribute_id: call get_attributes first to discover ids. positive selects the observation direction and defaults to true when omitted. Days with no data are returned as zeros, so the series is gap-free and chartable. Denominators are per-side, not the day's whole response set: brand_percentage divides by that day's responses that mention the brand (and had attribute extraction run), and competitor_percentage by responses that mention the tracked competitors. brand_total and competitor_total return those denominators explicitly. Competitor figures aggregate all tracked competitors unless competitor_ids narrows them.

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": {
      "website_id": "<website_id>",
      "filters": "<filters>",
      "attribute_id": "<attribute_id>"
    },
    "name": "get_attribute_time_series"
  }
}

Input Schema

Name	Required	Description	Default
`filters`	Yes
`positive`	No
`website_id`	Yes
`attribute_id`	Yes

Output Schema

Name	Required	Description	Default
`data`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 16, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Goes well beyond the readOnly/destructive/openWorld annotations by disclosing non-obvious behavior: gap-free zero-filled series, per-side denominators, exactly what brand_percentage/competitor_percentage divide by, and that brand_total/competitor_total return those denominators explicitly. This is precisely the context an agent needs to interpret results correctly.

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?

Front-loads the core purpose and prerequisite before the denser denominator mechanics. The denominator explanation is long but earns its place given the non-obvious metric definitions. Slightly verbose overall, so not a full 5.

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?

Given an output schema exists, the description needn't re-explain return fields, yet it usefully clarifies the denominator semantics behind key fields. Combined with the prerequisite and default behavior, nothing an agent needs to invoke or interpret the tool correctly is missing.

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?

Top-level schema coverage is 0%, so the description compensates by explaining attribute_id (must come from get_attributes) and positive (observation direction, defaults true). website_id is left implicit and filters is only covered by the richly-described nested schema, leaving one gap.

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?

States a specific verb and resource: the daily time series for ONE brand trait, with brand and competitors side by side. The 'daily trend' framing clearly separates it from get_attribute_metrics and get_attribute_observations without naming them. An agent can identify the exact output shape from the first sentence.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit prerequisite: an attribute_id is required and get_attributes must be called first to discover ids. It also clarifies the optional 'positive' default. It stops short of an explicit when-not/alternative statement (e.g. vs get_attribute_metrics), so it earns a 4 rather than a 5.

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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