Ein MCP-Server, der Coding-Agenten strukturierte Pflanzendaten für die Gartenplanung liefert: Pflegeprofile pro Art, Begleitpflanzen-Checks, die sich selbst erklären, und Pflanzfenster anhand von Frostdaten, die aus zehn Jahren beobachteter Wetterdaten abgeleitet werden.
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.Ein Modell, das nach Gartenplanung gefragt wird, gibt bereitwillig plausible Pflanzabstände und Ratschläge zur Begleitpflanzung zum Besten. Der meiste Teil davon ist Folklore, und nichts davon ist nachvollziehbar. Drei Dinge sprechen dafür, einen Server zu bauen statt eines Prompts, dem man vertraut:
Die Daten sind echt und belegt. Jeder Datensatz nennt, woher er stammt und unter welcher Lizenz.
Die Lücken sind sichtbar. Wenn eine übergeordnete Quelle ein Feld zurückhält, wird das in der Antwort auch gesagt. Wenn keine Quelle einen Wert veröffentlicht, bleibt er null, mit einer Erläuterung – statt stillschweigend aufgefüllt zu werden.
Urteile tragen ihre Begründung in sich. companion_check sagt nie „passt gut zusammen“, nur weil ein Gartenblog das behauptet. Es nennt einen Grund – aus Belegen, die in den Pflegedaten vorliegen – und gibt zu, wenn es keinerlei Belege hat.
Related MCP server: mcp-hazards
Tool | Funktion |
| Durchsucht den Artenkatalog nach Namen und liefert Kandidaten mit IDs. |
| Pflegeprofil: Sonne, Wasser, Winterhärtezonen, Enderhöhe, Essbarkeit, Toxizität für Menschen und Tiere, bekannte Schädlinge. |
| Ob zwei Pflanzen zusammengehören, mit allen illustrierdenden Gründen hinter dem Ergebnis. |
| Frostfenster, Saisonlänge und physiologische USDA-Zonen-Eignung eines Orts, aus beobachteten Tagesminima. |
| Experimental, standardmäßig aus. Identifiziert eine Pflanze anhand von Bild-URLs. Erfordert Perenual-Beta-Zugang. |
Jedes Tool liefert kompaktes JSON. Weder HTML noch Ausgabenumbruch.
companion_check liefert Mechanismen, keine AusredenKeine API auf der Quellenliste dieses Servers veröffentlicht Gegnerschaften zwischen Pflanzen – Permapeople enthält ausschließlich „companion to“-Links; ein Ja/Nein/Weiß nicht kann daher nicht einfach nachgesehen werden. Statt Gartenfolklore zu wiederholen, wird der negative Fall aus den bereits in den Pflegedaten vorhandenen Belegen abgeleitet:
Mechanismus | Befund | Begründung |
| schlecht | Gleiche botanische Familie: gleiche bodenbürftige Krankheiten, gleiche Fruchtfolge-Slot. |
| schlecht | Überlappende Schädlingsanfälligkeit: gemeinsame Kultur konzentriert den Schädling. |
| gut | In Permapeople dokumentiert (erfordert, dass jene Quelle sein konfiguriert). |
Ein Risikomechanismus hat Vorrang vor einer positiven Auflistung, aber jedes zutreffende Ergebnis wird geliefert, auch das überschattete. So bleibt ein gemischtes Ergebnispaar durchschaubar. Ein neutrales Befund sagt klar, dass es fehlende Belege sind – nicht ein Beleg für Verträglichkeit.
planting_window rät niemals PflanztermineGib ihm Koordinaten, und er leitet aus zehn ganzen Jahren täglicher Open-Meteo-Minima den median letzten Frost im Frühjahr, den ersten Frost im Herbst, die Saisonlänge und die USDA-Zone des Standorts ab – und prüft das gegen den veröffentlichten Winterhärte-Bereich der Pflanze. Verifiziert gegen veröffentlichte Normalwerte:
Standort | Letzter Frost | Erster Frost | Saison | Abgeleitete Zone |
Minneapolis, MN | 04-28 | 10-28 | 183 Tage | 4b |
Portland, OR | 03-17 | 11-25 | 225 Tage | 8b |
Honolulu, HI | — | — | — | 13a (frostfrei) |
Sydney, AU | — | — | — | 10b (frostfrei) |
Was er nicht tut: den Anbau-Teil erfinden. Keine Quelle in dieser Aufstellung veröffentlicht Reifedauer oder eine Klasse der Frosthärte. Von daher erscheinen Pflanz und Aussatz mineral. Also appears, when you supply days_to_maturity and frost_tolerance yourself. Diese Teilung ist gewollt: dieser Server bedient die Klima-Hälfte, die anrufende Seite die Krop-Hälfte.
Standorte auf südlichen Halbkugel werden in ihrer anbau. Gruppiert man dagegen nach Kalenderjahr, schneidet man einen südlichen Sommer entzwei und bekommt ein frostfreiungsfenster von auf grob inclusive, es ein Tag.
Benötigt Node 20 oder neuer und einen kostenlosen Perenual-API-Key.
npm install -g plant-intel-mcpOder launchen Sie es direkt aus dem Repo:
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 der claude_desktop_config.json:
{
"mcpServers": {
"plant-intel": {
"command": "npx",
"args": ["-y", "plant-intel-mcp"],
"env": {
"PERENUAL_API_KEY": "your-key"
}
}
}
}Variable | Erforderlich | Zweck |
| Ja | Artendaten. Kostenloser Schlüssel. |
| nein | Ergänzt vorhandene Begleitlisten. Kostenlos, Selbstbedienung. |
| nein | Auf |
| nein | Cache-Speicherort. Standardeinstellung unter |
| nein | Auf |
Der fehlende Perenual-Key beendet den Server nicht. Die Tools bleiben aufgelistet und jeder Aufruf antwortet mit der gesetzten Variable und dem Hinweisen auf einen Schlüsselanbieter.
Klarheit unsächlich, weil diese Einschränkungen formen, was der Server wahrhaftig beantworten kann.
Quelle | Lizenz | Grenzen, die zählen |
API-Termine; kostenloser Tarif nicht kommerziell | 100 requests/day. Kostenlose Schlüssel decken nur Art-Identifikatoren 1–3000, nicht die beworbene 10.000+. Pflegeanleitungen und Härtekarten sind kostenpflichtig. Zurückgehaltene Felder liefern Upgrade-Hinweise, die die Server entfernt. | |
CC BY-SA 4.0 | Optional, standardmäßig aus. Nur positive Companion-Links – keine Gegner. Ihre „Zulieferzustand“ umfasst PFAcfefg and Kew, deren Bedingungen restriktiver sind als CC BY-SA; bitte selbst prüfen, Commercial usage. | |
CC BY 4.0 | Free, ohne key. ERA5-Reanalyse auf „~~9-km-Raster“ – keine Stationsdaten. Fruhtermine können von örtlichen Normen abweichen, besonders in Hügel-, Gebirgs- oder küstennaher. Jede |
Wegen der Obergrenze 100/Tag cached der Server aggressiv (30 Tage für Artendaten, 90 für Wetterarchive) und hält ein Tagesbudget ein. Er weigert sich, eine Abfrage zu „verbrennen“, die im Budget nicht existiert, und sagt mit, wann das Budget zurückgesetzt wird, wenn er Anfrage abweist. Cache-Treffer werden nicht angerechnet.
plant_details liefert niemals Pflanzabstand. Keine Quelle hier publizert das, und eine Null mit Punktung ist nützlicher als eine Zahl, die keiner vertretet.
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 quotaDie Live-Suite existiert zur Erkennung von Abweichungen. Die Fixtures wurden aus der gesamten Publikation erstellt, daher wird überprüft, dass jedes Feld, das die Map-Route liest, noch in obenliegenden vorhandenen. Die Open-Meteo-Tests brauchen keine Credentials und laufen überall.
Siehe CLAUDE für Architektur und Konventionen.
MIT. Siehe LICENSE.
Daten, die über diesen Server geladen werden, unterliegen weiterhin den von der Quelle – sie sind oben eingelistet. Insbesondere gilt: Perenuale-Nutfenster ist nicht kommerziell, und Permapeople-Daten bleiben „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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