Pineprompt

Get Position Time Series Tool

get-position-time-series-tool

Read-onlyIdempotent

Average position of each tracked brand within the answers that mention it, per bucket. Position counts brands in the order they appear in an answer, so a lower number is more prominent and 1 is the first brand named. Scope by brand_id for one line; by segments for that segment's brands; otherwise top top_n by mention count (default 20, max 50). Per-bucket value is null when the brand had no mentions in that bucket.

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": {
      "project_id": "<project_id>",
      "start_date": "<start_date>",
      "end_date": "<end_date>"
    },
    "name": "get-position-time-series-tool"
  }
}

Input Schema

Name	Required	Description
`tags`	No	Tag IDs (prompt classifiers) to limit responses by. Optional.
`top_n`	No	Cap on series count when no brand/segments scope is set (default 20, max 50).
`brand_id`	No	Optional brand to scope the chart to (single-brand line). Must belong to the project.
`end_date`	Yes	Inclusive UTC end of the analytics window. Must be on or after `start_date` and on or before today.
`interval`	No	Bucket granularity for time-series tools. One of: day, week, month, year. Required for time-series tools, ignored for aggregate tools.
`monitors`	No	Monitor IDs in the active project. Optional.
`segments`	No	Segment IDs (brand groupings) to slice analytics by. Optional.
`countries`	No	ISO-3166-1 alpha-2 country codes to include. Optional.
`platforms`	No	AI platforms to include. Optional. Values: chatgpt, perplexity, gemini, grok, copilot, claude, google_ai_mode, google_ai_overview.
`project_id`	Yes	Project to compute analytics for.
`start_date`	Yes	Inclusive UTC start of the analytics window. ISO 8601 date (YYYY-MM-DD).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 15, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds genuinely useful behavior: position is an ordinal count with 1 = first brand named, the default/max for `top_n`, and that per-bucket values are null when a brand had no mentions. It doesn't describe ordering of series or how ties are handled, which keeps it out of the top band.

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?

Three dense sentences, front-loaded with the metric definition before scoping rules and the null-handling caveat. Every sentence carries information, though the scoping sentence compresses three cases into one long clause and could be split for faster scanning.

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 an 11-parameter analytics tool with no output schema, the description covers the metric, the scoping branches, the cap, and the null-value case, which is enough to call it correctly. Filter semantics (tags, monitors, countries, platforms) are left entirely to the schema, and no output shape is given beyond per-bucket values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so all 11 parameters are already documented in structured form, including the top_n default/max and the brand/segments scoping note. The description reinforces the precedence between `brand_id`, `segments`, and `top_n` but adds little beyond what the schema already states; baseline 3 applies.

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 the exact metric ('Average position of each tracked brand within the answers that mention it, per bucket') and even defines how position is computed, so an agent can distinguish it from the sibling sentiment/share-of-voice/visibility time-series tools without opening any schema.

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?

Gives explicit branch logic for scoping: use `brand_id` for a single line, `segments` for a segment's brands, otherwise fall back to `top_n` by mention count with the default and cap stated. It stops short of saying when to choose this tool over the sibling time-series tools, but the intra-tool routing is clear.

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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