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Atmospore

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list_supported_species

Retrieve the list of pollen species tracked by the model, including display names, categories, and concentration thresholds for risk levels. Validate species names before using other tools.

Instructions

List all pollen species the model tracks, with metadata.

Use this if the user asks "what species do you cover?" or to validate a species name before using it in another tool. Returns slug, display name, category (tree/grass/weed), localised display names (en, no, sv), and concentration thresholds for the risk levels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The list_supported_species() tool handler function. It calls client.species(), transforms each species into a dict with species slug, display_name, category, multilingual names, and risk thresholds, and wraps the call with _safe_call for structured error handling.
    @mcp.tool(description=LIST_SUPPORTED_SPECIES_DESCRIPTION)
    async def list_supported_species() -> dict[str, Any]:
        async def call() -> Any:
            species = await client.species()
            return [
                {
                    "species": s.species,
                    "display_name": s.display_name,
                    "category": s.category,
                    "names": s.names,
                    "risk_thresholds": s.risk_thresholds,
                }
                for s in species
            ]
    
        return await _safe_call(call())
  • Description string (LIST_SUPPORTED_SPECIES_DESCRIPTION) used as the tool description for the LLM. Documents that it returns slug, display name, category, localised names, and risk thresholds.
    LIST_SUPPORTED_SPECIES_DESCRIPTION = """List all pollen species the model tracks, with metadata.
    
    Use this if the user asks "what species do you cover?" or to validate a species name before
    using it in another tool. Returns slug, display name, category (tree/grass/weed), localised
    display names (en, no, sv), and concentration thresholds for the risk levels."""
  • Registration of list_supported_species as an MCP tool via the @mcp.tool(description=LIST_SUPPORTED_SPECIES_DESCRIPTION) decorator inside build_server().
    @mcp.tool(description=LIST_SUPPORTED_SPECIES_DESCRIPTION)
    async def list_supported_species() -> dict[str, Any]:
  • Reference in the help resource listing list_supported_species() as an available tool.
    | `list_supported_species()` | What species are tracked, with multilingual names |
  • Test for list_supported_species that verifies the tool returns correct data shape (species slug, multilingual names, etc.) when called.
    async def test_list_supported_species(server):
        payload = {
            "data": {
                "birch": {
                    "display_name": "Birch",
                    "category": "tree",
                    "names": {"en": "Birch", "no": "Bjørk", "sv": "Björk"},
                    "risk_thresholds": [15, 90, 500, 1500],
                }
            }
        }
        tool = _get_tool(server, "list_supported_species")
    
        with aioresponses() as m:
            m.get(f"{BASE}/species", payload=payload)
            result = await tool()
    
        assert result["ok"] is True
        assert len(result["data"]) == 1
        s = result["data"][0]
        assert s["species"] == "birch"
        assert s["names"] == {"en": "Birch", "no": "Bjørk", "sv": "Björk"}
Behavior4/5

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

No annotations provided, so the description carries full burden. It details the return fields (slug, display name, category, localized names, concentration thresholds), but doesn't mention any potential side effects or performance characteristics. Adequate for a read-only list operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with action, each sentence adds value. No redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no parameters and an output schema present, the description fully explains the tool's purpose and return data. It also provides usage context, making it complete for a simple list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters, so schema coverage is 100%. Baseline 4 applies as the description does not need to add parameter information.

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 clearly states 'List all pollen species the model tracks', with a specific verb and resource. It distinguishes itself from siblings like get_pollen and get_area_average by focusing on the supported species list with metadata.

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?

Provides explicit scenarios: 'Use this if the user asks "what species do you cover?" or to validate a species name before using it in another tool.' No mentions of when not to use, but for a list tool, this is sufficient.

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

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