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Augmented-Nature

Unofficial PubChem MCP Server

predict_admet_properties

Predict ADMET properties (Absorption, Distribution, Metabolism, Excretion, Toxicity) for chemical compounds using PubChem CID or SMILES string to assess drug viability and safety.

Instructions

Predict ADMET properties (Absorption, Distribution, Metabolism, Excretion, Toxicity)

Input Schema

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

Implementation Reference

  • The main handler function for the 'predict_admet_properties' tool. Currently implemented as a placeholder that returns a message indicating ADMET prediction is not yet implemented.
    private async handlePredictAdmetProperties(args: any) {
      return { content: [{ type: 'text', text: JSON.stringify({ message: 'ADMET prediction not yet implemented', args }, null, 2) }] };
    }
  • The tool registration entry including name, description, and input schema definition returned in ListToolsRequestSchema.
    {
      name: 'predict_admet_properties',
      description: 'Predict ADMET properties (Absorption, Distribution, Metabolism, Excretion, Toxicity)',
      inputSchema: {
        type: 'object',
        properties: {
          cid: { type: ['number', 'string'], description: 'PubChem Compound ID (CID)' },
          smiles: { type: 'string', description: 'SMILES string (alternative to CID)' },
        },
        required: [],
      },
    },
  • src/index.ts:770-771 (registration)
    Dispatch case in the CallToolRequestSchema handler that routes calls to the tool handler.
    case 'predict_admet_properties':
      return await this.handlePredictAdmetProperties(args);
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool predicts ADMET properties but doesn't explain how the prediction works, what models or data it uses, potential accuracy or limitations, rate limits, or authentication needs. For a prediction tool with zero annotation coverage, this is a significant gap in transparency.

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 without any wasted words. It's appropriately sized for the tool's complexity, making it easy to parse and understand 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 the complexity of predicting ADMET properties, the description is incomplete. There are no annotations, no output schema to explain return values, and the description lacks details on behavioral aspects like prediction methods or limitations. This makes it inadequate for an agent to fully understand how to use the tool effectively.

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?

The input schema has 100% description coverage, with clear documentation for 'cid' (PubChem Compound ID) and 'smiles' (SMILES string). The description doesn't add any meaning beyond this, such as explaining the relationship between these parameters or usage examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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: predicting ADMET properties (Absorption, Distribution, Metabolism, Excretion, Toxicity). It specifies the verb 'predict' and the resource 'ADMET properties,' making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'assess_drug_likeness' or 'get_toxicity_info,' which might overlap in scope, so it doesn't reach a score of 5.

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?

The description provides no guidance on when to use this tool versus alternatives. With sibling tools such as 'assess_drug_likeness' and 'get_toxicity_info,' which may relate to ADMET aspects, there's no indication of context, prerequisites, or exclusions. This lack of usage instructions leaves the agent to infer based on tool names alone.

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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