eo-mcp

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monitor_crop_phenology

Track crop growth stages and detect drought stress via Sentinel-2 vegetation indices, providing season dates and vigor anomalies.

Instructions

Monitor agricultural crop phenology, growth trajectories, and vegetative anomalies across seasonal cycles. Tracks Start of Season (SOS), Peak of Season (POS), End of Season (EOS), and drought stress vs baseline. Zero-config: Automatically samples cloud-free Sentinel-2 observations across the agricultural calendar.

Args: bbox: Bounding box [min_lon, min_lat, max_lon, max_lat] in WGS84. year: Observation year (default 2024). crop_type: Optional crop descriptor (e.g. 'Wheat', 'Maize', 'Vineyard', 'Olives'). format: Output format ('summary', 'geojson', or 'csv').

Returns: JSON or formatted string with Start/Peak/End of Season dates, peak NDVI, seasonal biomass proxy, and crop vigor anomaly evaluation.

References:

Reed, B. C., et al. (1994). Journal of Vegetation Science, 5(5), 703-714. DOI: 10.2307/3235884
Zhang, X., et al. (2003). Remote Sensing of Environment, 84(3), 471-475. DOI: 10.1016/S0034-4257(02)00135-9
Jönsson, P., & Eklundh, L. (2004). Computers & Geosciences, 30(8), 833-845. DOI: 10.1016/j.cageo.2004.05.006

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": {
      "bbox": "<bbox>"
    },
    "name": "monitor_crop_phenology"
  }
}

Input Schema

Name	Required	Default
`bbox`	Yes
`year`	No
`format`	No	summary
`crop_type`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 23, 2026

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does add context by noting 'Automatically samples cloud-free Sentinel-2 observations' and 'Zero-config,' which tells the agent the tool handles data selection. However, it does not mention any limitations, processing time, data availability constraints, or whether the operation is purely read-only in terms of external side effects.

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 well-structured with clear sections: purpose, args, returns, references. The essential information is front-loaded. The scientific references at the end add credibility but do not help an agent select or invoke the tool, so they keep it from a perfect conciseness score.

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 tool has an output schema and only one required parameter, the description is largely complete: it covers all parameters, the return summary, and the automatic Sentinel-2 sampling behavior. However, it omits details like valid year ranges, bbox size limits, or error conditions, which would be useful for an agent handling edge cases.

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's Args section fully compensates. It explains bbox format (WGS84 coordinates), year default, crop_type examples, and format enum values ('summary', 'geojson', 'csv'). This gives the agent everything needed to set each parameter correctly, far 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 opens with a specific verb and resource: 'Monitor agricultural crop phenology, growth trajectories, and vegetative anomalies across seasonal cycles.' It further specifies exact outputs (SOS, POS, EOS, drought stress vs baseline), making it clearly distinct from sibling tools like calculate_spectral_index or analyze_reservoir_drought. An agent can confidently recognize this as the tool for crop growing-cycle analysis.

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?

The description conveys clear context: use this tool when you need to monitor crop phenology, seasonal growth, or vegetative anomalies. The 'Zero-config' note suggests it is a turnkey option, but it does not explicitly mention when not to use it or name alternative tools, so it stops short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.