bc_count_drugs_by_field
Count unique values in FDA-approved drug fields for statistical analysis. Specify a field like 'sponsor_name' or 'dosage_form' to get term frequencies.
Instructions
Count unique values in a field across FDA-approved drugs. Useful for statistical analysis.
Returns: dict: Results array with term and count for each unique value or error message.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| field | Yes | Field to count (e.g., 'sponsor_name', 'products.dosage_form', 'products.route', 'openfda.pharm_class_epc') | |
| search_filter | No | Optional search filter to apply before counting | |
| limit | No | Maximum number of count results to return |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- The primary handler function for the bc_count_drugs_by_field tool. It uses the @core_mcp.tool() decorator for registration and includes inline Pydantic schema validation via Annotated Fields. Queries the OpenFDA API to count unique values in specified drug fields.
@core_mcp.tool() def count_drugs_by_field( field: Annotated[ str, Field( description="Field to count (e.g., 'sponsor_name', 'products.dosage_form', 'products.route', 'openfda.pharm_class_epc')" ), ], search_filter: Annotated[ Optional[str], Field(description="Optional search filter to apply before counting") ] = None, limit: Annotated[int, Field(description="Maximum number of count results to return", ge=1, le=1000)] = 100, ) -> dict: """Count unique values in a field across FDA-approved drugs. Useful for statistical analysis. Returns: dict: Results array with term and count for each unique value or error message. """ # If field is an array, use .exact for correct counting array_fields = [ "openfda.brand_name", "openfda.generic_name", "openfda.manufacturer_name", "openfda.pharm_class_epc", "openfda.pharm_class_moa", "openfda.pharm_class_pe", "openfda.pharm_class_cs", "products.brand_name", ] count_field = field + ".exact" if field in array_fields and not field.endswith(".exact") else field url_params = {"count": count_field, "limit": limit} # Add search filter if provided if search_filter: url_params["search"] = search_filter # Build the complete URL base_url = "https://api.fda.gov/drug/drugsfda.json" try: response = requests.get(base_url, params=url_params) # type: ignore response.raise_for_status() return response.json() except requests.exceptions.RequestException as e: return {"error": f"Failed to fetch FDA drug count data: {e!s}"} - src/biocontext_kb/app.py:35-39 (registration)Registers the core_mcp server (containing the tool) into the main BioContextAI MCP application under the prefixed namespace 'bc' (from slugify('BC')), making the tool available as 'bc_count_drugs_by_field'.
for mcp in [core_mcp, *(await get_openapi_mcps())]: await mcp_app.import_server( mcp, slugify(mcp.name), ) - src/biocontext_kb/core/_server.py:3-7 (registration)Defines the core_mcp FastMCP instance named 'BC' where individual tools like count_drugs_by_field are registered via decorators. This MCP is later imported into the main app with 'bc' prefix.
core_mcp = FastMCP( # type: ignore "BC", instructions="Provides access to biomedical knowledge bases.", ) - Imports the openfda module, which triggers the loading and decorator-based registration of the count_drugs_by_field tool into core_mcp.
from .openfda import * - src/biocontext_kb/core/openfda/__init__.py:6-6 (registration)Exposes the count_drugs_by_field function for import, facilitating its registration when the openfda module is imported.
from ._count_drugs import count_drugs_by_field, get_drug_statistics