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biocontext-ai

BioContextAI Knowledgebase MCP

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bc_get_open_targets_query_examples

Retrieve example GraphQL queries for biomedical data exploration using the Open Targets API, demonstrating common use cases like target information, drug discovery, and disease associations.

Instructions

Retrieve example GraphQL queries for the Open Targets API. Examples demonstrate common use cases.

Returns: dict: Example queries mapped by category (informationForTarget, drugsForTarget, associatedDiseases, etc.) with GraphQL query strings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler function for the tool 'bc_get_open_targets_query_examples'. It returns a dictionary containing example GraphQL queries for various use cases on the Open Targets API, such as target information, associated diseases, drugs, etc.
    @core_mcp.tool()
    def get_open_targets_query_examples() -> dict:
        """Retrieve example GraphQL queries for the Open Targets API. Examples demonstrate common use cases.
    
        Returns:
            dict: Example queries mapped by category (informationForTarget, drugsForTarget, associatedDiseases, etc.) with GraphQL query strings.
        """
        return EXAMPLE_QUERIES
  • Import statement that loads the handler function, triggering its registration via the @core_mcp.tool() decorator.
    from ._get_open_targets_query_examples import get_open_targets_query_examples
  • Initialization of the FastMCP server with prefix 'BC', which prefixes all tool names (e.g., 'bc_' + function_name).
    core_mcp = FastMCP(  # type: ignore
        "BC",
        instructions="Provides access to biomedical knowledge bases.",
    )
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the return type (dict with categorized GraphQL query strings) and that it's a retrieval operation, which implies read-only behavior. However, it doesn't mention potential limitations like rate limits, authentication needs, or whether the examples are static or dynamically generated, leaving gaps in behavioral context.

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

Conciseness5/5

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

The description is front-loaded with the core purpose in the first sentence, followed by output details. Both sentences earn their place by clarifying the tool's function and return value without any wasted words. It's efficiently structured for quick understanding.

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

Completeness4/5

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

Given the tool's low complexity (0 parameters) and the presence of an output schema, the description is reasonably complete. It explains what the tool does and the structure of the output (categorized dict with GraphQL strings). However, without annotations, it could benefit from more behavioral details like usage constraints or example freshness, but the output schema likely covers return specifics adequately.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter details, focusing instead on the output. This meets the baseline of 4 for zero-parameter tools, as it avoids unnecessary repetition of schema information.

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's purpose: 'Retrieve example GraphQL queries for the Open Targets API' with specific examples of categories. It distinguishes from siblings like 'bc_query_open_targets_graphql' (which likely executes queries) by focusing on examples. However, it doesn't explicitly contrast with 'bc_get_open_targets_graphql_schema' (which might provide schema structure rather than queries).

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

Usage Guidelines3/5

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

The description implies usage context through 'Examples demonstrate common use cases,' suggesting this tool is for learning or reference. However, it lacks explicit guidance on when to use this versus alternatives like 'bc_query_open_targets_graphql' (for actual querying) or 'bc_get_open_targets_graphql_schema' (for schema details). No exclusions or prerequisites are mentioned.

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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