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BioContextAI Knowledgebase MCP

BioContextAI Knowledgebase MCP

Official
by biocontext-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

bc_get_generic_equivalents

Find generic equivalents for a brand name drug by searching ANDA entries with matching active ingredients.

Instructions

Find generic equivalents for a brand name drug. Searches ANDA entries with matching active ingredients.

Returns: dict: Brand drug info, generic_equivalents array, total_generics_found count or error message.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "brand_name": "<brand_name>"
    },
    "name": "bc_get_generic_equivalents"
  }
}

Input Schema

NameRequiredDescriptionDefault
brand_nameYesBrand name drug to find generics for

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.0
    • changedInput schema / properties / brand_name / description
      Previous value: -"Brand name drug to find generic equivalents for"New value: +"Brand name drug to find generics for"
    • removedInput schema / properties / brand_name / title
      Removed value: -"Brand Name"
  2. First observed

TDQS

A4.1/5.0
Behavior3/5

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

The description discloses it searches ANDA entries, implying a read operation. However, it does not detail any behavioral traits like required permissions or limitations, such as requiring exact brand name matching.

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 concise with two clear sentences: one for purpose and one for return structure. No unnecessary words.

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 tool's simplicity, the description explains the return format and the search logic, making it complete for the agent's selection and invocation.

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

Parameters3/5

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

Schema coverage is 100%, and the description adds context ('brand name drug') but does not significantly enhance meaning beyond the schema's description.

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 verb 'Find generic equivalents for a brand name drug' and specifies the resource (ANDA entries). It effectively distinguishes from sibling tools like bc_get_drug_by_application_number and bc_search_drugs_fda.

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 indicates when to use the tool (when you have a brand name drug and need generics), but lacks explicit guidance on when not to use it or alternatives.

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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