An MCP server that hands coding agents structured, source-cited plant data for garden planning — species care profiles, mechanism-backed companion verdicts, and frost-date planting windows.
search_plants — look up species by common or scientific name, filtered by life cycle, edibility, or indoor suitability; returns candidate ids for use in the other tools.
plant_details — full care profile for one species: sun, water, hardiness zones, mature height, edibility, toxicity to people and pets, and known pests; withheld fields come back null and named in notes.
companion_check — decide whether two plants should share a bed, returning every mechanism behind the verdict: shared botanical family or overlapping pests (bad), documented Permapeople listing (good), with overridden reasons still shown.
planting_window — derive last spring frost, first autumn frost, season length, and USDA zone from ten years of observed daily minima at a location (coordinates or a zone), then check the plant's hardiness fit; add days_to_maturity and frost_tolerance to get sow-by and transplant dates.
Pass ids rather than names to avoid extra upstream requests; every tool returns compact JSON, no HTML or page dumps.
All tools are read-only and answer honestly — explaining gaps and refusing to guess spacing or crop timing rather than filling in folklore.
An experimental identify_plant (image URLs) exists but is off by default and not registered here.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@plant-intel-mcp is it safe to plant tomatoes next to potatoes? also check dill"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that gives coding agents structured plant data for garden planning: species care profiles, companion checks that explain themselves, and frost-date planting windows derived from ten years of observed weather.
Live demo: rootwise — an AI gardening advisor built on this server, showing every tool call and source it uses (source).
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.A model asked about garden planning will happily produce plausible spacing numbers and companion-planting advice. Most of it is folklore, and none of it is traceable. Three things make this worth a server rather than a prompt:
The data is real and cited. Every record says where it came from and under what licence.
The gaps are visible. When an upstream tier withholds a field, the response says so. When no source publishes a number, it stays null with an explanation instead of being filled in.
Verdicts carry mechanisms. companion_check never says "these are good
together" because a gardening blog said so. It says why, from evidence in
the care data, and admits when it has no evidence at all.
Related MCP server: farmbot-agent
Tool | What it does |
| Search the species catalogue by name; returns candidates with ids. |
| Care profile: sun, water, hardiness zones, mature height, edibility, toxicity to people and pets, known pests. |
| Whether two plants belong near each other, with every mechanism behind the verdict. |
| Frost envelope, season length, and USDA zone fit for a site, from observed daily minima. |
| Experimental, off by default. Identify a plant from image URLs. Requires Perenual beta access. |
Every tool returns compact JSON. No HTML, no page dumps.
companion_check returns mechanisms, not folkloreNo API in this server's source roster publishes plant antagonists — Permapeople lists "companion to" links only — so a good/bad/neutral answer cannot simply be looked up. Rather than restate garden lore, the negative case is derived from evidence already in the care data:
Mechanism | Verdict | Reasoning |
| bad | Same botanical family: shared soilborne disease, same rotation slot. |
| bad | Overlapping pest susceptibility: co-planting concentrates the pest. |
| good | Documented in Permapeople (requires that source to be configured). |
A risk mechanism outranks a positive listing, but every matched reason is
returned, including the overridden one, so a mixed pair stays legible. A
neutral verdict states plainly that it is an absence of evidence, not
evidence of compatibility.
planting_window will not guess crop timingGive it coordinates and it derives, from ten whole years of Open-Meteo daily minima, the median last spring frost, first autumn frost, season length, and the site's USDA zone — then checks that against the plant's published hardiness range. Verified against published normals:
Site | Last frost | First frost | Season | Derived zone |
Minneapolis, MN | 04-28 | 10-28 | 183 days | 4b |
Portland, OR | 03-17 | 11-25 | 252 days | 8b |
Honolulu, HI | — | — | — | 13a (frost free) |
Sydney, AU | — | — | — | 10b (frost free) |
What it will not do is invent the crop half. No source in this roster
publishes days-to-maturity or a frost-hardiness class, so transplant and sow-by
dates appear only when you supply days_to_maturity and frost_tolerance
yourself. That division is deliberate: this server owns the climate half, and
the caller owns the crop half.
Southern-hemisphere sites are read on their own growing season. Grouping by calendar year splits a southern summer in two and reports a frost-free window of roughly one day.
Requires Node 20 or newer and a free Perenual API key.
npm install -g plant-intel-mcpOr run it without installing: npx plant-intel-mcp.
Or run it straight from the 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 claude_desktop_config.json:
{
"mcpServers": {
"plant-intel": {
"command": "npx",
"args": ["-y", "plant-intel-mcp"],
"env": {
"PERENUAL_API_KEY": "your-key"
}
}
}
}Variable | Required | Purpose |
| yes | Species data. Free key. |
| no | Adds documented companion listings. Free, self-service. |
| no | Set to |
| no | Cache location. Defaults under |
| no | Set to |
Missing the Perenual key does not stop the server. The tools stay listed and each call answers with the variable to set and where to get a key.
Stated plainly, because these constraints shape what the server can honestly answer.
Source | Licence | Limits that matter |
API terms; free tier is non-commercial | 100 requests/day. Free keys cover species ids 1–3000 only, not the advertised 10,000+ — and in practice most common garden crops sit above that line (tomato is 8759). Search covers the full catalogue; details do not, and an out-of-range request comes back as a 429 that this server translates rather than reporting as throttling. Care guides and hardiness maps are paid. Withheld fields return upgrade prompts, which this server strips. | |
CC BY-SA 4.0 | Optional, off unless configured. Positive companion links only — no antagonists. Its upstream includes PFAF and Kew, whose terms are more restrictive than CC BY-SA implies; check before relying on it commercially. | |
CC BY 4.0 | Free, no key. ERA5 reanalysis on a ~9 km grid — not station data. Expect frost dates to differ from local normals, especially in hills, valleys, or near water. Every |
Because of the 100/day ceiling, the server caches aggressively (30 days for species data, 90 for weather archives) and guards a daily budget, refusing to spend a request it does not have and naming the reset time when it declines. Cache hits are never charged against it.
plant_details never returns spacing. No source here publishes it, and a null
with a note is more useful than a number nobody stands behind.
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 suite exists for drift detection. Fixtures were built from published documentation, so it asserts that every field the mapper reads is still present upstream -- last confirmed against the live API on 2026-09-08. Its Open-Meteo tests need no credential and run anywhere.
See CLAUDE.md for architecture and conventions.
MIT. See LICENSE.
Data retrieved through this server remains under its own source's terms — see the table above. In particular, Perenual's free tier is non-commercial, and Permapeople's data 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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