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Augmented-Nature

Unofficial PubChem MCP Server

assess_drug_likeness

Evaluate chemical compounds for drug-likeness using Lipinski Rule of Five, Veber rules, and PAINS filters to identify promising candidates for pharmaceutical development.

Instructions

Assess drug-likeness using Lipinski Rule of Five, Veber rules, and PAINS filters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cidNoPubChem Compound ID (CID)
smilesNoSMILES string (alternative to CID)

Implementation Reference

  • The handler function that executes the logic for the 'assess_drug_likeness' tool. Currently implemented as a placeholder returning a 'not yet implemented' message.
    private async handleAssessDrugLikeness(args: any) {
      return { content: [{ type: 'text', text: JSON.stringify({ message: 'Drug-likeness assessment not yet implemented', args }, null, 2) }] };
    }
  • src/index.ts:539-550 (registration)
    Registration of the 'assess_drug_likeness' tool in the list of available tools, including name, description, and input schema.
    {
      name: 'assess_drug_likeness',
      description: 'Assess drug-likeness using Lipinski Rule of Five, Veber rules, and PAINS filters',
      inputSchema: {
        type: 'object',
        properties: {
          cid: { type: ['number', 'string'], description: 'PubChem Compound ID (CID)' },
          smiles: { type: 'string', description: 'SMILES string (alternative to CID)' },
        },
        required: [],
      },
    },
  • Dispatch case in the tool call handler that routes 'assess_drug_likeness' calls to the specific handler method.
    case 'assess_drug_likeness':
      return await this.handleAssessDrugLikeness(args);
  • Input schema definition for the 'assess_drug_likeness' tool, specifying optional cid or smiles parameters.
    inputSchema: {
      type: 'object',
      properties: {
        cid: { type: ['number', 'string'], description: 'PubChem Compound ID (CID)' },
        smiles: { type: 'string', description: 'SMILES string (alternative to CID)' },
      },
      required: [],
    },
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 assessment methods but doesn't disclose behavioral traits such as computational requirements, typical runtime, output format, or any limitations (e.g., handling of specific compound types). This leaves gaps in understanding how the tool operates beyond its stated purpose.

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 a single, efficient sentence that front-loads the core purpose and lists key methods without unnecessary details. Every word earns its place, making it easy to parse quickly.

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

Completeness2/5

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

Given no annotations, no output schema, and a tool that performs assessment (implying analysis with potential complexity), the description is incomplete. It lacks details on what the assessment returns (e.g., scores, flags, recommendations), how results are interpreted, or any error conditions. This limits usability for an AI agent.

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 description coverage is 100%, so parameters 'cid' and 'smiles' are fully documented in the schema. The description adds no additional parameter semantics beyond implying input is a compound (via CID or SMILES). Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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: assessing drug-likeness using specific methods (Lipinski Rule of Five, Veber rules, PAINS filters). It specifies the verb 'assess' and the resource 'drug-likeness', but doesn't explicitly differentiate from siblings like 'predict_admet_properties' or 'get_compound_properties' which might overlap in pharmaceutical assessment domains.

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. The description lists methods but doesn't indicate scenarios, prerequisites, or exclusions. For example, it doesn't clarify if this is for early-stage screening versus detailed analysis, or how it differs from sibling tools like 'predict_admet_properties'.

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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