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usa-npn-mcp-server

magnitude-phenometrics

Quantify the intensity and abundance of phenological activity across time and space to analyze synchrony, peak timing, and climate change impacts on species interactions.

Instructions

About the tool: Summarizes the intensity and abundance of phenological activity across multiple individuals, sites, or time periods using aggregated status and intensity data. Shows 'how much' phenological activity is occurring (not just when), providing insights into the magnitude, synchrony, and temporal patterns of biological processes.

When to use: Understanding broad ecological patterns, studying synchrony between interacting species, analyzing peak activity timing, or investigating how environmental changes affect the intensity of biological processes across populations.

Key applications:

  • Species synchrony analysis: Quantifying how synchronized phenological timing is between interacting species (pollinators and plants, herbivores and host plants, predators and prey)

  • Peak activity timing: Identifying when maximum biological activity occurs across populations

  • Climate change impacts: Studying how warming affects the magnitude and timing of phenological events

  • Biodiversity patterns: Understanding temporal overlap in species activity within ecosystems

  • Population-level responses: Analyzing how abundant or widespread phenological activity is across landscapes

  • Conservation planning: Identifying critical timing windows for species management

Scientific context: Based on current research showing that phenological synchrony between species is shifting due to climate change, with implications for ecosystem functioning and species interactions. This tool helps quantify these critical ecological relationships.

Requires: Date range and frequency parameters (daily, weekly, etc.) are essential. Recommended to specify species and phenophases of interest to avoid overwhelming results. Research applications:

  • 'Are migrating birds arriving when their insect food sources are most abundant?'

  • 'How synchronous is flowering across plant species in prairie communities?'

  • 'Has climate change affected the temporal overlap between butterfly emergence and host plant activity?'

Data interpretation: Results show time-series data of phenological abundance/intensity aggregated by specified frequency. Values represent proportion of 'yes' records, animal abundance measures, or intensity metrics across the selected populations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateYesStart date in YYYY-MM-DD format. Must be used with end_date.
end_dateYesEnd date in YYYY-MM-DD format. Must be used with start_date.
bottom_left_x1NoX coordinate of the bottom left corner for bounding box filtering.
bottom_left_y1NoY coordinate of the bottom left corner for bounding box filtering.
upper_right_x2NoX coordinate of the upper right corner for bounding box filtering.
upper_right_y2NoY coordinate of the upper right corner for bounding box filtering.
species_idNoUnique species identifier.
station_idNoUnique identifier associated with an observer’s location.
species_typeNoSpecies type(s) the organism belongs to. Must match values from getAnimalTypes and getPlantTypes.
networkNoName of the network(s)/group(s) where the organism is observed. Must match values from getPartnerNetworks.
stateNoState where the observation occurred. Uses two-character postal abbreviation.
phenophase_categoryNoPhenophase category. Must match values from getPhenophase.
phenophase_idNoUnique identifier of the phenophase.
functional_typeNoFunctional types of the species. Must match values from getSpeciesFunctionalTypes.
climate_dataNoFlag to indicate whether all climate data fields should be returned. Accepts 0 or 1. Almost always beneficial to see climate data in relation to phenometric data.
frequencyYesNumber of days by which to delineate the period of time. Should be less or equal to number of days between start_date and end_date.
additional_fieldNoAdditional fields to include in output.
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It mentions that results show time-series data of phenological abundance aggregated by frequency, but does not disclose whether the tool is read-only, has authentication requirements, or causes side effects. Key behavioral traits like idempotency or data source limitations are absent.

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

Conciseness3/5

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

The description is quite long with multiple sections (about, when to use, key applications, scientific context, etc.). While well-structured and informative, it could be more concise. Approximately 200 words, which is acceptable but exceeds the typical length for a tool description.

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?

Given the large number of parameters (17) and no output schema, the description provides rich scientific context, data interpretation guidance, and example research questions. However, it lacks explicit details on the exact structure of the output (e.g., fields, pagination). This is a minor gap but still informative overall.

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?

All 17 parameters have descriptions in the schema (100% coverage). The description adds value by emphasizing the necessity of date range and frequency, recommending species and phenophase specification, and explaining the climate_data flag. It also provides context for how parameters relate to scientific use cases, going beyond the raw 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 identifies the tool as summarizing the intensity and abundance of phenological activity across multiple individuals, sites, or time periods. It explicitly distinguishes itself from siblings by focusing on 'how much' rather than 'when', and provides concrete examples like synchrony analysis and peak activity timing.

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?

The description includes a 'When to use' section listing applications (e.g., species synchrony, climate change impacts), but does not specify when not to use this tool or mention alternative sibling tools. It gives clear context for use but lacks exclusions or direct comparisons.

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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