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
`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)

Implementation Reference

src/biocontext_kb/core/stringdb/_get_string_similarity_scores.py:10-43 (handler)

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.",
)
```

src/biocontext_kb/core/stringdb/__init__.py:1-11 (helper)

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",
]

BioContextAI Knowledgebase MCP