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MCP server for FYTA plant sensor data providing comprehensive plant health monitoring, analysis, and care recommendations
FYTA server has moderate definition quality with some critical gaps. All 7 tools have descriptions and basic schemas, but several lack required parameter descriptions, output documentation, and actionable error handling. Tool naming follows verb_noun conventions (get_*, check_*, diagnose_*, analyze_*), which is good. However, parameter documentation is inconsistent, some params lack descriptions entirely, violating the pattern:tool-description rule. No output schemas are documented, making it unclear what fields agents should expect. Error handling is absent from visible code, no recovery guidance, no retryable vs fatal classifications. The tools are mostly read-only and simple, which keeps scores from being lower, but lack of parameter documentation and output schema definition prevents a higher score.
Analyze plant photos for visual health indicators like leaf color, damage, or pest signs. Requires image data as base64 or URL.
Perform intelligent health diagnosis for a specific plant. Analyzes current sensor status, historical trends, and thresholds to provide: - Overall health assessment (critical/poor/fair/good/excellent) - Main issues identified with detailed explanations - Trend analysis from historical data - Confidence score based on data quality and recency This is the most powerful tool for understanding plant health and automation decisions.
Get all plants with their current sensor data including moisture, temperature, light, and nutrient status. Returns comprehensive data about each plant including status, sensor readings, and garden information.
Retrieve the history of care actions logged for a plant including watering, fertilizing, repotting, and other interventions.
Get an overview of all gardens with plant counts and summary. Organizes plants by their assigned gardens.
Get detailed information about a specific plant by ID. Includes scientific name, nickname, sensor data, status indicators, and optimal conditions.
No output schemas documented. Tools return unspecified JSON, agents cannot plan downstream calls or extract required fields. All 7 tools lack response documentation.
get_all_plants and get_plant_events lack pagination parameters (limit, offset/cursor). If plant count is large, response may blow context window. No documented result limits.
No error handling or recovery guidance visible in handlers.py or tools.py. No categorization of retryable vs fatal errors. LLM has no guidance when a call fails.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 14 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 35 | - | v1 |
Advanced DLI (Daily Light Integral) analysis - goes beyond basic FYTA data. Provides intelligent light management insights: - Daily DLI calculations and trends over time - Chronic deficit detection (days/weeks below optimal) - Precise grow light recommendations with specs and costs - Seasonal predictions (next 3 months forecast) - Energy cost estimates for supplemental lighting - Placement and duration recommendations This is THE tool for optimizing plant lighting and avoiding light stress.
Get recent plant events and alerts including warnings and critical issues. Summarizes what needs attention across all plants with severity levels and timestamps.
Get historical measurements and sensor data for a specific plant by ID. Returns time-series data including temperature, light, moisture, and nutrient measurements over time. Useful for tracking trends and plant health history. You can specify a timeline (hour, day, week, month) to control the time range.
Comprehensive statistical analysis of plant sensor data. Provides deep insights into historical patterns and behaviors: - Descriptive statistics (mean, median, std deviation, min/max, percentiles) - Anomaly detection (outliers, unusual patterns) - Optimal ranges derived from actual plant performance - Variability analysis (stability vs fluctuations) - Correlation analysis between different metrics - Day/night cycle patterns Perfect for understanding plant behavior patterns and setting realistic thresholds.
Analyze historical trends for a specific plant's sensor data. Calculates slopes, direction, and forecasts for temperature, light, moisture, and nutrients. Helps predict issues before they become critical: - Detects declining moisture before soil dries out completely - Identifies gradual temperature changes - Predicts when intervention is needed - Reduces false alarms by analyzing patterns over time Essential for proactive plant care and automation.
Get a list of plants that need attention based on their status indicators. Returns plants with non-optimal moisture, temperature, light, or nutrient levels (status != 2).
Get intelligent watering schedule based on soil moisture trends and plant requirements. Predicts when each plant will need water based on current trends and historical patterns.
Log manual care actions that FYTA doesn't track automatically. Track watering, fertilizing, repotting, pruning, and more. This fills FYTA's biggest gap - knowing what YOU did! Enables: - Correlation analysis (Did watering help?) - Frequency tracking (Water every X days) - Smart recommendations based on YOUR care history - Learning what works
get_all_plants and get_plant_events input schemas are empty ({}). No parameters are defined, but description does not clarify what filters/options apply (if any).
get_plant_measurements timeline parameter accepts enum values (hour, day, week, month) but description does not state these are the only valid options. LLM may hallucinate other values like 'year'.
Tool definitions split across multiple files (tools.py, client.py, examples/api_wrapper.py) and not all visible in source. diagnose_plant defined in examples/api_wrapper.py suggests tool registration may not be complete in main server.py.