Un servidor MCP que ofrece a los agentes de codificación datos estructurados sobre plantas para la planificación de jardines: perfiles de cuidado de especies, comprobaciones de cultivo asociado que se explican por sí mismas y ventanas de plantación basadas en fechas de heladas derivadas de diez años de datos meteorológicos observados.
companion_check("tomato", "potato")
-> bad: both are Solanaceae. Same-family crops share soilborne diseases and
compete for the same rotation slot, so plant them apart and rotate the bed.Un modelo al que se le pregunte sobre planificación de jardines producirá encantado cifras de separación plausibles y consejos de cultivo asociado. La mayor parte es folclore, y nada de ello es trazable. Hay tres razones por las que esto merece un servidor en lugar de un prompt:
Los datos son reales y están citados. Cada registro indica de dónde procede y bajo qué licencia.
Las carencias son visibles. Cuando un proveedor de la cadena no entrega un campo, la respuesta lo indica. Cuando ninguna fuente publica un número, este permanece como null con una explicación, en lugar de inventarse.
Los veredictos incluyen mecanismos. companion_check nunca dice que "estas plantas son buenas juntas" porque un blog de jardinería lo haya afirmado. Dice el porqué, a partir de la evidencia en los datos de cuidado, y admite cuando no tiene evidencia alguna.
Related MCP server: mcp-hazards
Herramienta | Qué hace |
| Busca en el catálogo de especies por nombre; devuelve candidatos con sus ids. |
| Perfil de cuidado: sol, agua, zonas de rusticidad, altura en madurez, comestibilidad, toxicidad para personas y mascotas, plagas conocidas. |
| Si dos plantas deben estar cerca la una de la otra, con todos los mecanismos detrás del veredicto. |
| Intervalo de heladas, duración de la temporada y ajuste a la zona USDA de un lugar, a partir de las mínimas diarias observadas. |
| Experimental, desactivado por defecto. Identifica una planta desde URLs de imágenes. Requiere acceso beta de Perenual. |
Cada herramienta devuelve JSON compacto. Sin HTML ni volcados de página.
companion_check devuelve mecanismos, no folcloreNinguna API del repertorio de fuentes de este servidor publica antagonistas de plantas (Permapeople solo enlista vínculos "compañera de"), así que una respuesta buena/mala/neutral no se puede simplemente consultar. En lugar de repetir el saber popular de jardín, el caso negativo se deriva de la evidencia que ya está en los datos de cuidado:
Mecanismo | Veredicto | Razonamiento |
| bad | Misma familia botánica: enfermedad transmitida por el suelo compartida, misma rotación. |
| bad | Susceptibilidad a las plagas que se superpone: cultivar juntas concentra la plaga. |
| good | Documentado en Permapeople (requiere que esa fuente esté configurada). |
Un mecanismo de riesgo tiene prioridad sobre una entrada positiva, pero se devuelve todos los motivos coincidentes, incluido el superado, de modo que una pareja mixta siga siendo legible. Un veredicto neutral establece claramente que se trata de una ausencia de evidencia, no de una evidencia de compatibilidad.
planting_window no adivinará el momento del cultivoDanle coordenadas y, a partir de diez años completos de mínimas diarias de Open-Meteo, se obtiene la mediana de the última helada de primavera, la primera helada de otoño, la longitud de la temporada y la zona USDA del lugar; y después se contrasta con el rango de rusticidad publicado para la planta. Verificado frente a los normales publicados:
Lugar | Ultima helada | Primera helada | Temporada | Zona derivada |
Minneapolis, MN | 04-28 | 10-28 | 183 días | 4b |
Portland, OR | 03-17 | 11-25 | 252 días | 8b |
Honolulu, HI | — | — | — | 13a (by free heladas) |
Sydney, AU | — | — | — | 10b (by que free heladas) |
Lo que no no es el no hacer es inventar la parte del cultivo. Ninguna fuente de este conjunto publica los días hasta la madurez ni una clase de rusticidad de heladas, así que las fechas de sembrado y de trasplante aparecen solo si tú proporcionas days_to_maturity y frost_tolerance. Esa división es deliberada: este servidor posee la mitad climática, and the llamador posee la mitad del cultivo.
Los lugares del hemisferio sur se lo que les su propia tempora de cultivo. Agrupar por por año calendario del idir un verano del sur en dos partos y arrojía una ventana libre de heladas de aproximadamente un día.
Requiere Node 20 or newer and a free Perenual API key.
npm install -g plant-intel-mcpOofrom the repo direct:
git clone https://github.com/wyattlindsey/plant-intel-mcp.git && cd plant-intel-mcp && npm install && npm run buildclaude mcp add plant-intel --env PERENUAL_API_KEY=your-key -- npx -y plant-intel-mcpIn claude_desktop_config.json:
{
"mcpServers": {
"plant-intel": {
"command": "npx",
"args": ["-y", "plant-intel-mcp"],
"env": {
"PERENUAL_API_KEY": "your-key"
}
}
}
}Variable | Requerido | Propósito |
| yes | Datos de especies. Clave gratis. |
| no | Añade listados de acompañamiento documentados. Gratis, self-service. |
| no | Define en |
| no | Location de la caché. Por defecto en |
| no | Define en |
La falta de la key de Perenual no detiene el servidor. The tools stay listed and each call answer with the variable to set and where to get a key.
Declarado vontad, porque estas restricciones conforman lo que the servidor puede responder honestamente.
Fuente | Licencia | Límites que importan |
Términos de la API; free tier is non-commercial | 100 peticiones/día. Las claves gratis cubres los ids de especies 1–3000 únicamente, no los 10,000+ publicitados. Las guías de cuidado y los mapas de rusticidad son de pago. Los campos retenidos devuelven promps de upgrade, que this server eliminates. | |
CC BY-SA 4.0 | Optional, off unless configurado. Positivos solo companion links — no antagonists. Su upstream include PFAF y Kew, whose terms son que restrictivos de lo que CC BY-SA implies; check antes de relaición en comercial. | |
CC BY 4.0 | Free, no API key. ERA5 reanalysis on a ~9 km grid — no es datos de estación. Mostra que las fechas de heladas diffieran de las normales, especially en colinas, vales o near water. Cada respuesta de |
Por el límite de 100/día, el servidor cachea de forma intransigente (30 días para datos de especies, 90 días para archivos de meteorología) y controla un presupuesto diario, rechazando gastar una petición que no tiene y nombrando el momento del reset cuando la rechaza. Los hits de caché nunca se cargan contra su presupuesto.
plant_details nunca devuelve espaciado. Ninguna fuente de este roster publicita eso, and null with a note más útil que un número que no respaldada—
npm test # unit and protocol tests, no network
npm run typecheck
npm run build
npm run test:live # opt-in; hits real APIs and spends quotaThe live test suite exists to detect desviaciones. Fixtures were built a partir de documentation publicada, so it asserts that every field that mapper map sigue presente. Upstream. Sus tests Open-Meteo no requieren credenciales y se ejecutan anywhere.
See CLAUDE.md for architecture and conventions.
MIT. See LICENSE.
Datos retrieved through this server remain under the terms of their own source — see the table above. In particular, the free level of Perenual is non-commercial, and the data of Permapeople is share-alike.
Whether two plants should share a bed, with the mechanism behind the answer. A "bad" verdict is derived from shared botanical family (rotation and soilborne disease) or overlapping pest susceptibility, never from folklore. A "good" verdict comes from a documented Permapeople listing when that source is configured. Every matched reason is returned, including any the verdict overrode.
| Name | Required | Description | Default |
|---|---|---|---|
| plant_a | Yes | A species id from search_plants, or a name. | |
| plant_b | Yes | The plant to check against plant_a. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and openWorldHint, but the description adds substantial behavioral detail: the mechanism behind 'bad' (botanical family, pest susceptibility, not folklore) and 'good' (Permapeople listing when configured), plus the return behavior (every matched reason returned, including overridden ones). This goes beyond annotations and provides clear expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and then providing mechanism and return behavior. No redundant words; every clause adds value. Excellent conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the purpose, the logic behind verdicts, and what is returned. With no output schema, it sufficiently covers the expected result behavior. It doesn't state the exact return format, but that's not essential for a read-only check with clear semantics. It's nearly complete, missing only minor format specifics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema descriptions cover both parameters clearly: plant_a as a species id or name, plant_b as the plant to check. The description itself adds no extra parameter semantics, but the mention of 'species id from search_plants' in the schema and the overall context is adequate. Baseline 3 is appropriate given 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks whether two plants should share a bed, explaining the criteria for 'bad' and 'good' verdicts. It distinguishes itself from siblings (search_plants, plant_details, planting_window) by focusing on companion compatibility, not search, details, or timing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context implies when to use: to check plant compatibility. The plant_a parameter mentions 'A species id from search_plants', linking to a sibling for finding ids, and the description mentions 'when that source is configured', a prerequisite. However, it doesn't explicitly state when NOT to use it or contrast with alternatives like plant_details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full care profile for one species: sun, water, hardiness zones, mature height, edibility, toxicity to people and pets, and known pests. Fields the configured Perenual plan withholds are reported as null and named in notes rather than left silently absent.
| Name | Required | Description | Default |
|---|---|---|---|
| plant | Yes | A species id from search_plants (e.g. "perenual:1852") or a name. An id is cheaper: a name costs an extra upstream request to resolve. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description adds meaningful behavior: fields withheld by the configured plan are returned as null and named in `notes` rather than silently omitted. This is useful transparency about response shape, though it does not cover error cases or other edge behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose and immediately followed by the most important behavioral caveat. No filler or repetition of structured metadata.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description compensates by enumerating the profile contents and explaining the null/notes handling for plan-withheld fields. For a single-parameter read-only tool, this is complete and actionable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single `plant` parameter is already fully described in the schema, including accepted formats and the cost difference between an id and a name. The description adds no additional parameter-level meaning, so the schema carries the burden and baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states exactly what it does: 'Full care profile for one species' and enumerates concrete content areas (sun, water, hardiness zones, mature height, edibility, toxicity, pests). This clearly distinguishes it from siblings like search_plants, planting_window, and companion_check.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (when you need a full one-species care profile) and the schema parameter explains how to supply a plant via search_plants id or name. However, it does not explicitly state when to prefer plant_details over alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frost envelope and hardiness fit for a plant at a location. Given coordinates it derives last spring frost, first autumn frost, season length, and the USDA zone from ten years of observed daily minima. Supply days_to_maturity and frost_tolerance from your own crop data to also get transplant and sow-by dates -- no source this server reads publishes those, so they are never guessed.
| Name | Required | Description | Default |
|---|---|---|---|
| plant | Yes | A species id from search_plants, or a name. | |
| latitude | No | Site latitude in decimal degrees. | |
| longitude | No | Site longitude in decimal degrees. | |
| percentile | No | Higher is more cautious: later spring frost, earlier autumn frost. Defaults to 50. | |
| hardiness_zone | No | A USDA zone such as "7a", used only when coordinates are unavailable. A zone encodes winter minimum temperature, not frost timing, so no frost dates can be derived from it. | |
| frost_tolerance | No | Crop frost hardiness. Defaults to "tender", the cautious assumption. | |
| days_to_maturity | No | Days from sowing or transplant to first harvest. No source this server reads publishes this, so supply it from your own crop data to get a sow-by date. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description clearly discloses the data source ('ten years of observed daily minima') and honestly states that 'no source this server reads publishes those, so they are never guessed' regarding days_to_maturity and frost_tolerance. It also explains that hardiness_zone alone cannot provide frost dates fires. The annotations (readOnlyHint, openWorldHint) are consistent. The description adds substantial behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence that efficiently conveys the purpose, data source, and optional parameters. Nofluff, no repetition, and structured logically from core function to optional enhancements.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main inputs and outputs, data source, and limitations (no guessing of maturity/tolerance). It doesn't specify output format, but that's not strictly necessary. It also doesn't mention error cases, but the schema covers bounds. Overall, it's informative and sufficient for an agent to decide and use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 7 parameters have schema descriptions, with 100% coverage. The tool description adds context by explaining the role of days_to_maturity and frost_tolerance (user-supplied) and the advisory note that they are never guessed. It also clarifies that hardiness_zone is only used when coordinates are unavailable. This goes beyond the schema, enhancing parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb+resource: 'Frost envelope and hardiness for a location.' It clearly states what the tool derives: last spring frost, first autumn frost, season length, and USDA zone from coordinates. It also differentiates from sibling tools (search_plants, plant_details, companion_check) by focusing on climatic data for planting, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance on when to supply `days_to_maturity` and `frost_tolerance` to obtain transplant and sow-by dates, and notes that these are not sourced from the server. It implies that coordinates are preferred, and that hardiness_zone is only a fallback. It does not explicitly contrast with sibling tools, but the tool's specific inputs (coordinates, plant) and outputs (frost dates, zone) make usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the Perenual species catalogue by name and return candidate species with ids. Use the returned id with plant_details, companion_check, or planting_window rather than passing a name again -- resolving a name costs an extra upstream request.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 1-based page of results. Defaults to 1. | |
| cycle | No | Restrict to a life cycle. | |
| query | Yes | Common or scientific name, e.g. "tomato" or "Solanum". | |
| edible | No | Restrict to species with edible parts. | |
| indoor | No | Restrict to species suited to indoor growing. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description correctly aligns with those. It adds useful behavioral context beyond annotations by noting that resolving a name costs an extra upstream request, which is important for an agent deciding whether to cache or reuse ids. Without output schema, it doesn't describe pagination or result limits, but with strong annotations, the bar is lower and it provides relevant cost information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the verb and resource, and each sentence earns its place. The first sentence states purpose and output; the second provides essential usage guidance and a cost note. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 params, all documented in schema), the description covers the core value: search and return ids, plus a key behavioral note about cost. It doesn't discuss pagination or result structure, but without an output schema, that's not required. The description is complete enough for a search-list tool with strong annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all five parameters with descriptions. The description adds little beyond the schema: it mentions 'search by name' but doesn't elaborate on the filter parameters. Per the rubric, since schema coverage is high, baseline is 3, and the description doesn't add extra value beyond the schema's parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the Perenual species catalogue by name and returns candidate species with ids. It distinguishes itself by mentioning that the returned id should be used with sibling tools (plant_details, companion_check, planting_window) rather than passing a name again, which highlights its role as a lookup step.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly guides when to use this tool: to resolve a name into an id, and when not to: avoid passing a name to downstream tools. It names the specific alternatives (plant_details, companion_check, planting_window) and explains the cost of not using the id, providing clear exclusionary context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.2.0companion_checkplant_detailsplanting_windowsearch_plantsScored across 4 tools
Each tool targets a distinct operation: searching plants, retrieving details, computing planting windows, and checking companions. There is no overlap in purpose; the tools chain together via the search_then_detail flow, making selection unambiguous.
All tool names follow a consistent pattern: a noun ('plant') modified by a descriptor ('details', 'intel', 'window', 'check'). The names are clear, descriptive, and follow a predictable schema, with no mixed conventions or vague verbs.
Four tools is a lean but coherent set for a plant-intel domain. Each tool covers a distinct need (search, details, planting time, companionship), so none feel redundant. A fifth tool for bulk or batch queries might be additive, but the current count is appropriate for the niche scope.
The server covers the primary lifecycle: identify a plant, retrieve its profile, plan its planting window, and assess its compatibility with neighbors. Notable gaps include a missing tool for updating or managing the data (e.g., adding a custom plant or saving a garden plan), but the core research-oriented use case is fully covered.
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