bc_get_string_similarity_scores
Calculate protein similarity scores using STRING database Smith-Waterman bit scores to assess homology between protein pairs for biomedical research.
Instructions
Retrieve protein homology similarity scores from STRING database based on Smith-Waterman bit scores. Only scores above 50 reported.
Returns: list or dict: Similarity scores array with stringId_A, stringId_B, bitscore or error message.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| protein_symbol | Yes | First protein symbol (e.g., 'TP53') | |
| protein_symbol_comparison | Yes | Second protein symbol (e.g., 'MKI67') | |
| species | No | Species taxonomy ID (e.g., '9606' for human) |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- The handler function for the tool (likely 'bc_get_string_similarity_scores' under 'BC' MCP server). Includes schema via Pydantic Annotated fields, registration via @core_mcp.tool(), and core logic: resolves symbols to STRING IDs using get_string_id tool, queries STRING homology API for bitscores.
@core_mcp.tool() def get_string_similarity_scores( protein_symbol: Annotated[str, Field(description="First protein symbol (e.g., 'TP53')")], protein_symbol_comparison: Annotated[str, Field(description="Second protein symbol (e.g., 'MKI67')")], species: Annotated[str, Field(description="Species taxonomy ID (e.g., '9606' for human)")] = "", ) -> Union[List[Dict[str, Any]], dict]: """Retrieve protein homology similarity scores from STRING database based on Smith-Waterman bit scores. Only scores above 50 reported. Returns: list or dict: Similarity scores array with stringId_A, stringId_B, bitscore or error message. """ # Resolve both protein symbols to STRING IDs try: string_id1 = get_string_id.fn(protein_symbol=protein_symbol, species=species) string_id2 = get_string_id.fn(protein_symbol=protein_symbol_comparison, species=species) if not all(isinstance(string_id, str) for string_id in [string_id1, string_id2]): return {"error": "Could not extract STRING IDs"} identifiers = f"{string_id1}%0d{string_id2}" url = f"https://string-db.org/api/json/homology?identifiers={identifiers}" if species: url += f"&species={species}" response = requests.get(url) response.raise_for_status() return response.json() except requests.exceptions.RequestException as e: return {"error": f"Failed to fetch similarity scores: {e!s}"} except Exception as e: return {"error": f"An error occurred: {e!s}"} - src/biocontext_kb/core/__init__.py:19-19 (registration)Import statement that loads the stringdb module, executing the @tool decorators to register get_string_similarity_scores to core_mcp.
from .stringdb import * - src/biocontext_kb/core/_server.py:3-6 (registration)Definition of the core_mcp FastMCP server instance (named 'BC') where all tools including get_string_similarity_scores are registered.
core_mcp = FastMCP( # type: ignore "BC", instructions="Provides access to biomedical knowledge bases.", ) - Module __init__.py that re-exports the get_string_similarity_scores function (and others) for convenient import.
from ._get_string_id import get_string_id from ._get_string_interactions import get_string_interactions from ._get_string_network_image import get_string_network_image from ._get_string_similarity_scores import get_string_similarity_scores __all__ = [ "get_string_id", "get_string_interactions", "get_string_network_image", "get_string_similarity_scores", ]