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Noctua MCP Server

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by geneontology

search_models

Search Gene Ontology Causal Activity Models (GO-CAMs) by title, state, contributor, group, publication, or gene product to find specific models with metadata.

Instructions

Search for GO-CAM models based on various criteria.

Allows searching models by title, state, contributor, group, publication, or gene product. Returns a list of matching models with their metadata.

Args: title: Search for models containing this text in their title state: Filter by model state (production, development, internal_test) contributor: Filter by contributor ORCID (e.g., 'https://orcid.org/0000-0002-6601-2165') group: Filter by group/provider (e.g., 'http://www.wormbase.org') pmid: Filter by PubMed ID (e.g., 'PMID:12345678') gene_product: Filter by gene product (e.g., 'UniProtKB:Q9BRQ8', 'MGI:MGI:97490') limit: Maximum number of results to return (default: 50) offset: Offset for pagination (default: 0)

Returns: Dictionary containing search results with model metadata

Examples: # Search for all production models results = search_models(state="production")

# Find models containing "Wnt signaling" in title
results = search_models(title="Wnt signaling")

# Find models for a specific gene product
results = search_models(gene_product="UniProtKB:P38398")

# Find models from a specific paper
results = search_models(pmid="PMID:30194302")

# Find models by a specific contributor
results = search_models(
    contributor="https://orcid.org/0000-0002-6601-2165"
)

# Combine filters
results = search_models(
    state="production",
    title="kinase",
    limit=10
)

# Pagination example
page1 = search_models(limit=50, offset=0)
page2 = search_models(limit=50, offset=50)

# Find models from specific research group
results = search_models(group="http://www.wormbase.org")

# Search for development models with specific gene
results = search_models(
    state="development",
    gene_product="MGI:MGI:97490"
)

Notes: - Results include model ID, title, state, contributors, and dates - Use pagination (offset/limit) for large result sets - Filters can be combined for more specific searches - Gene products can be from various databases (UniProt, MGI, RGD, etc.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNo
stateNo
contributorNo
groupNo
pmidNo
gene_productNo
limitNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The search_models MCP tool handler function. Decorated with @mcp.tool(), it accepts optional filters (title, state, contributor, group, pmid, gene_product, limit, offset) and delegates to the BaristaClient.list_models() method. Returns search results or error dict on exception.
    @mcp.tool()
    async def search_models(
        title: Optional[str] = None,
        state: Optional[str] = None,
        contributor: Optional[str] = None,
        group: Optional[str] = None,
        pmid: Optional[str] = None,
        gene_product: Optional[str] = None,
        limit: int = 50,
        offset: int = 0
    ) -> Dict[str, Any]:
        """
        Search for GO-CAM models based on various criteria.
    
        Allows searching models by title, state, contributor, group, publication, or gene product.
        Returns a list of matching models with their metadata.
    
        Args:
            title: Search for models containing this text in their title
            state: Filter by model state (production, development, internal_test)
            contributor: Filter by contributor ORCID (e.g., 'https://orcid.org/0000-0002-6601-2165')
            group: Filter by group/provider (e.g., 'http://www.wormbase.org')
            pmid: Filter by PubMed ID (e.g., 'PMID:12345678')
            gene_product: Filter by gene product (e.g., 'UniProtKB:Q9BRQ8', 'MGI:MGI:97490')
            limit: Maximum number of results to return (default: 50)
            offset: Offset for pagination (default: 0)
    
        Returns:
            Dictionary containing search results with model metadata
    
        Examples:
            # Search for all production models
            results = search_models(state="production")
    
            # Find models containing "Wnt signaling" in title
            results = search_models(title="Wnt signaling")
    
            # Find models for a specific gene product
            results = search_models(gene_product="UniProtKB:P38398")
    
            # Find models from a specific paper
            results = search_models(pmid="PMID:30194302")
    
            # Find models by a specific contributor
            results = search_models(
                contributor="https://orcid.org/0000-0002-6601-2165"
            )
    
            # Combine filters
            results = search_models(
                state="production",
                title="kinase",
                limit=10
            )
    
            # Pagination example
            page1 = search_models(limit=50, offset=0)
            page2 = search_models(limit=50, offset=50)
    
            # Find models from specific research group
            results = search_models(group="http://www.wormbase.org")
    
            # Search for development models with specific gene
            results = search_models(
                state="development",
                gene_product="MGI:MGI:97490"
            )
    
        Notes:
            - Results include model ID, title, state, contributors, and dates
            - Use pagination (offset/limit) for large result sets
            - Filters can be combined for more specific searches
            - Gene products can be from various databases (UniProt, MGI, RGD, etc.)
        """
        client = get_client()
    
        try:
            results = client.list_models(
                title=title,
                state=state,
                contributor=contributor,
                group=group,
                pmid=pmid,
                gp=gene_product,
                limit=limit,
                offset=offset
            )
            return results
        except Exception as e:
            return {
                "error": "Failed to search models",
                "message": str(e)
            }
  • Registration of search_models as an MCP tool via @mcp.tool() decorator on line 1347.
    @mcp.tool()
Behavior4/5

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

With no annotations, the description fully describes the tool's behavior: it performs a search, returns a dictionary with model metadata, and supports pagination. It notes that results include specific fields. No destructive actions mentioned, which is appropriate for a search tool.

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

Conciseness4/5

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

The description is well-structured with sections (Args, Returns, Examples, Notes) and is thorough. However, it is somewhat lengthy with many examples; it could be slightly more concise without losing clarity.

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 presence of an output schema (context signal), the description adequately covers the return value ('dictionary with model metadata') and mentions fields included. All parameters are fully documented with examples, making it complete.

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?

The description provides detailed explanations for all 8 parameters, including expected formats (e.g., ORCID, UniProtKB) and default values. It includes multiple examples demonstrating parameter usage, compensating for the 0% schema description coverage.

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 'Search for GO-CAM models based on various criteria' and lists specific filtering options (title, state, contributor, etc.). It distinguishes this tool from sibling tools like 'search_annotations' and 'search_bioentities' by focusing on 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 includes explicit examples of when to use each filter and pagination. It provides notes on combining filters and using pagination for large result sets. However, it does not explicitly state when not to use this tool or compare it to alternative search 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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