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search_for_variants

Query genetic variants in gnomAD databases to retrieve variant IDs and information using dataset, reference genome, and search parameters.

Instructions

[gnomAD API] Search for variants (v2/v3/v4) Args: dataset (str): gnomAD dataset ID (gnomad_r4/gnomad_r3/gnomad_r2_1) reference_genome (str): Reference genome (GRCh37 or GRCh38) query (str): Search string (variant_id) Returns: dict: search results (variant_id)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetYes
reference_genomeYes
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The `search_for_variants` tool is defined here as an MCP tool, using `run_query_with_metadata` to fetch data.
    def search_for_variants(
        dataset: str,
        reference_genome: str,
        query: str
    ) -> dict:
        """
        [gnomAD API] Search for variants (v2/v3/v4)
        Args:
            dataset (str): gnomAD dataset ID (gnomad_r4/gnomad_r3/gnomad_r2_1)
            reference_genome (str): Reference genome (GRCh37 or GRCh38)
            query (str): Search string (variant_id)
        Returns:
            dict: search results (variant_id)
        """
        variables = {
            'dataset': dataset,
            'reference_genome': reference_genome,
            'query': query,
        }
        return run_query_with_metadata('variant_search', variables)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the API and returns a dict with variant_id, but lacks details on behavioral traits like rate limits, authentication needs, error handling, pagination, or what the search results include beyond variant_id. This is inadequate for a search tool with no annotation coverage.

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 appropriately sized and front-loaded, starting with the core purpose. The Args/Returns structure is clear, but the bracketed '[gnomAD API]' could be integrated more smoothly, and it's slightly verbose in listing dataset examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 3 parameters with 0% schema coverage and an output schema exists (returns dict), the description is moderately complete. It explains parameters well but lacks behavioral context and usage guidelines. The output schema handles return values, so no need to detail them further, but overall it's adequate with clear gaps.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining each parameter: dataset as gnomAD dataset ID with examples, reference_genome as genome type with examples, and query as a search string for variant_id. This clarifies semantics beyond the bare schema, though it could provide more context on valid values or search syntax.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for variants using the gnomAD API, specifying it works with v2/v3/v4 datasets. It distinguishes from siblings like 'search_for_genes' by focusing on variants, though it doesn't explicitly differentiate from 'get_variant_info' which might retrieve specific variant details rather than search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't clarify if this should be used for broad searches versus 'get_variant_info' for detailed info on known variants, or how it relates to other variant-related tools like 'get_clinvar_variant_info'.

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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