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geneontology

Noctua MCP Server

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by geneontology

search_bioentities

Search for genes and proteins using Gene Ontology data with filters for organism, type, and source database to find bioentity information.

Instructions

Search for bioentities (genes/proteins) using Gene Ontology data.

Searches across gene and protein names/labels with optional taxonomic filtering. Provides access to comprehensive bioentity information from GOlr.

Args: text: Text search across names and labels (e.g., "insulin", "kinase") taxon: Organism filter - accepts NCBI Taxon ID with or without prefix (e.g., "9606", "NCBITaxon:9606" for human) bioentity_type: Type filter (e.g., "protein", "gene") source: Source database filter (e.g., "UniProtKB", "MGI", "RGD") limit: Maximum number of results to return (default: 10) offset: Starting offset for pagination (default: 0)

Returns: Dictionary containing search results with bioentity information

Examples: # Search for human insulin proteins results = search_bioentities( text="insulin", taxon="9606", bioentity_type="protein" )

# Find mouse kinases from MGI
results = search_bioentities(
    text="kinase",
    taxon="NCBITaxon:10090",
    source="MGI",
    limit=20
)

# Search for any human genes/proteins
results = search_bioentities(
    taxon="9606",
    limit=50
)

# Find specific protein types
results = search_bioentities(
    text="receptor",
    bioentity_type="protein",
    limit=25
)

# Search across all organisms
results = search_bioentities(text="p53")

# Pagination example
page1 = search_bioentities(text="kinase", limit=10, offset=0)
page2 = search_bioentities(text="kinase", limit=10, offset=10)

# Common organisms:
# Human: "9606" or "NCBITaxon:9606"
# Mouse: "10090" or "NCBITaxon:10090"
# Rat: "10116" or "NCBITaxon:10116"
# Fly: "7227" or "NCBITaxon:7227"
# Worm: "6239" or "NCBITaxon:6239"
# Yeast: "559292" or "NCBITaxon:559292"

Notes: - Results include ID, name, type, organism, and source information - Text search covers both short names/symbols and full descriptions - Taxon IDs automatically handle NCBITaxon: prefix normalization - Use pagination for large result sets - Sources include UniProtKB, MGI, RGD, ZFIN, SGD, and others

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNo
taxonNo
bioentity_typeNo
sourceNo
limitNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The MCP tool handler for 'search_bioentities'. This async function defines the tool logic, normalizes the taxon input, uses AmigoClient to perform the search, formats the results, and handles exceptions. The @mcp.tool() decorator registers it as an MCP tool.
    @mcp.tool()
    async def search_bioentities(
        text: Optional[str] = None,
        taxon: Optional[str] = None,
        bioentity_type: Optional[str] = None,
        source: Optional[str] = None,
        limit: int = 10,
        offset: int = 0
    ) -> Dict[str, Any]:
        """
        Search for bioentities (genes/proteins) using Gene Ontology data.
    
        Searches across gene and protein names/labels with optional taxonomic filtering.
        Provides access to comprehensive bioentity information from GOlr.
    
        Args:
            text: Text search across names and labels (e.g., "insulin", "kinase")
            taxon: Organism filter - accepts NCBI Taxon ID with or without prefix
                   (e.g., "9606", "NCBITaxon:9606" for human)
            bioentity_type: Type filter (e.g., "protein", "gene")
            source: Source database filter (e.g., "UniProtKB", "MGI", "RGD")
            limit: Maximum number of results to return (default: 10)
            offset: Starting offset for pagination (default: 0)
    
        Returns:
            Dictionary containing search results with bioentity information
    
        Examples:
            # Search for human insulin proteins
            results = search_bioentities(
                text="insulin",
                taxon="9606",
                bioentity_type="protein"
            )
    
            # Find mouse kinases from MGI
            results = search_bioentities(
                text="kinase",
                taxon="NCBITaxon:10090",
                source="MGI",
                limit=20
            )
    
            # Search for any human genes/proteins
            results = search_bioentities(
                taxon="9606",
                limit=50
            )
    
            # Find specific protein types
            results = search_bioentities(
                text="receptor",
                bioentity_type="protein",
                limit=25
            )
    
            # Search across all organisms
            results = search_bioentities(text="p53")
    
            # Pagination example
            page1 = search_bioentities(text="kinase", limit=10, offset=0)
            page2 = search_bioentities(text="kinase", limit=10, offset=10)
    
            # Common organisms:
            # Human: "9606" or "NCBITaxon:9606"
            # Mouse: "10090" or "NCBITaxon:10090"
            # Rat: "10116" or "NCBITaxon:10116"
            # Fly: "7227" or "NCBITaxon:7227"
            # Worm: "6239" or "NCBITaxon:6239"
            # Yeast: "559292" or "NCBITaxon:559292"
    
        Notes:
            - Results include ID, name, type, organism, and source information
            - Text search covers both short names/symbols and full descriptions
            - Taxon IDs automatically handle NCBITaxon: prefix normalization
            - Use pagination for large result sets
            - Sources include UniProtKB, MGI, RGD, ZFIN, SGD, and others
        """
    
        # Normalize taxon ID - add NCBITaxon prefix if just a number
        if taxon and not taxon.startswith("NCBITaxon:"):
            if taxon.isdigit():
                taxon = f"NCBITaxon:{taxon}"
    
        try:
            with AmigoClient() as client:
                results = client.search_bioentities(
                    text=text,
                    taxon=taxon,
                    bioentity_type=bioentity_type,
                    source=source,
                    limit=limit,
                    offset=offset
                )
    
                return {
                    "results": [
                        {
                            "id": result.id,
                            "label": result.label,
                            "name": result.name,
                            "type": result.type,
                            "taxon": result.taxon,
                            "taxon_label": result.taxon_label,
                            "source": result.source
                        }
                        for result in results
                    ],
                    "count": len(results),
                    "limit": limit,
                    "offset": offset
                }
    
        except Exception as e:
            return {
                "error": "Failed to search bioentities",
                "message": str(e)
            }
  • The @mcp.tool() decorator registers the search_bioentities function as an MCP tool.
    @mcp.tool()
  • Import of AmigoClient used by the search_bioentities tool for querying bioentities.
    from noctua.amigo import AmigoClient
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: text search coverage (names and labels), taxon ID prefix normalization, pagination guidance for large result sets, and source database examples. It also describes what results include (ID, name, type, organism, source).

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

Conciseness3/5

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

The description is well-structured with clear sections (description, args, returns, examples, notes) but is quite lengthy with 7 detailed examples and extensive notes. While all content is valuable, it could be more front-loaded with the most critical information rather than burying key details in examples and notes.

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?

Given the complexity of 6 parameters with 0% schema coverage and no annotations, the description provides complete guidance including parameter semantics, behavioral context, examples, and notes about result content. The presence of an output schema means the description doesn't need to detail return values, and it adequately covers all other aspects.

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

Parameters5/5

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

With 0% schema description coverage for 6 parameters, the description fully compensates by providing detailed parameter documentation including purpose, examples, default values, and format specifications (e.g., taxon accepts NCBI Taxon ID with or without prefix). The 'Args' section comprehensively explains all parameters beyond what the bare schema provides.

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 the tool searches for bioentities (genes/proteins) using Gene Ontology data, specifying both the action (search) and resource (bioentities). It distinguishes from siblings like search_annotations and search_models by focusing specifically on bioentities rather than annotations or models.

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?

The description provides clear context for when to use this tool (searching across gene/protein names with optional taxonomic filtering) and includes extensive examples showing different use cases. However, it doesn't explicitly state when NOT to use it or mention specific alternatives among sibling tools.

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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