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VectorInstitute

usa-npn-mcp-server

status-intensity

Retrieve raw, unprocessed observation records showing daily phenological status and intensity for individual plants and animals.

Instructions

About the tool: Retrieves raw, unprocessed observation records from citizen and professional scientists documenting day-by-day phenological status (yes/no) and intensity measurements for individual plants and animal species. Each record represents a single observation event showing whether specific phenophases (like 'breaking leaf buds' or 'full bloom') were occurring on a particular date for a specific individual organism at a monitoring site.

When to use: Only for detailed analysis of specific observation events, quality control, or when you need the granular day-to-day data that underlies the aggregated metrics. Most users should use Individual, Site, or Magnitude Phenometrics instead.

Key applications: Data validation, understanding observer reporting patterns, analyzing day-to-day phenological transitions, custom aggregations not available in other tools. Performance warning: This tool can return massive datasets (potentially millions of records). Always limit queries to small date ranges (≤30 days recommended) and specific geographic areas or species to prevent system crashes. Use aggregated tools (Individual/Site/Magnitude Phenometrics) for broader analyses.

Data interpretation: Values of -9999 represent missing/null data. Records include observation date, individual ID, phenophase status, intensity measurements, and site metadata.

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.
additional_fieldNoAdditional fields to include in output.
Behavior4/5

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

No annotations provided, so the description carries full burden. It explains raw data nature, missing values (-9999), and includes a performance warning about massive datasets. Could explicitly state read-only nature but is comprehensive.

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?

Well-structured with sections for clarity, but somewhat lengthy. Every section adds value; could be slightly more concise but effective.

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 a complex tool with 16 parameters and no output schema, the description covers purpose, usage, performance, and data interpretation comprehensively. Lacks specific output format details but acceptable.

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?

Schema coverage is 100% with clear parameter descriptions. The description adds context by recommending small date ranges and bounding box filtering to prevent crashes, enhancing parameter understanding beyond 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 specifies that the tool retrieves raw, unprocessed observation records with phenological status and intensity. It contrasts with aggregated sibling tools like Individual Phenometrics, making the purpose distinct.

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?

Explicit 'When to use' and 'When not to use' sections with named alternatives (Individual/Site/Magnitude Phenometrics). Provides concrete applications like data validation and custom aggregations.

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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