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IBM
by IBM

stac_time_series

Extract time series of satellite band data for an area over a date range, returning per-date artifact references for change monitoring.

Instructions

Extract a time series of band data over an area.

Searches for all scenes in the date range, downloads the requested bands for each, and returns references to the full temporal stack.

Args: bbox: Area of interest [west, south, east, north] bands: Bands to extract (e.g., ["red", "nir"]) date_range: Date range "YYYY-MM-DD/YYYY-MM-DD" collection: STAC collection (default: sentinel-2-l2a) max_cloud_cover: Maximum cloud cover 0-100 (default: 20) max_items: Maximum scenes to include (default: 50) catalog: Catalog name (default: earth_search) output_mode: Response format - "json" (default) or "text"

Returns: JSON with per-date artifact references

Tips for LLMs: - Use for monitoring change over time (vegetation growth, flood extent, urban expansion) - Pair with stac_compute_index on each date's artifact for temporal index analysis (e.g., NDVI over a growing season) - Keep bbox small — each date downloads full band data - Use max_cloud_cover=10 for cleaner optical time series - For a single cloud-free image from a date range, use stac_temporal_composite with method="median" instead - max_items limits the number of dates; set higher for dense temporal sampling or lower to reduce download volume - Cloud cover filter is automatically skipped for non-optical collections (sentinel-1-grd, cop-dem-glo-30)

Example: ts = await stac_time_series( bbox=[0.85, 51.85, 0.95, 51.92], bands=["red", "nir"], date_range="2024-01-01/2024-12-31", max_cloud_cover=10 )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bboxYes
bandsYes
catalogNo
max_itemsNo
collectionNo
date_rangeYes
output_modeNojson
max_cloud_coverNo
Behavior4/5

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

No annotations are provided, so the description bears full responsibility. It describes the search and download process, notes that cloud cover filtering is skipped for non-optical collections, and implies read-only behavior. Some details like error handling or performance guarantees are missing, but overall it is thorough.

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?

The description is relatively long but well-structured with sections (Args, Returns, Tips, Example). The main purpose is front-loaded, and each sentence contributes value. Minor redundancy in tips (e.g., repeating max_items explanation) could be trimmed, but still efficient.

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 the complexity (8 parameters, no output schema, no annotations), the description is complete. It covers the tool's operation, usage constraints, return format, and includes a realistic example. No critical gaps.

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

Parameters5/5

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

Schema description coverage is 0%, but the description includes a detailed 'Args' section and 'Tips' that explain each parameter's purpose, default values, and behavior (e.g., max_cloud_cover 0-100, default 20). This adds significant meaning beyond the bare schema.

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 clearly states 'Extract a time series of band data over an area,' which is a specific verb and resource. It distinguishes from siblings like stac_temporal_composite, which produces a single composite image.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool (monitoring change over time) and when not to (single cloud-free image, recommending stac_temporal_composite). It also includes tips on bbox size, cloud cover, and max_items.

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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