Skip to main content
Glama

predict_admet_properties

Predict ADMET properties (Absorption, Distribution, Metabolism, Excretion, Toxicity) for chemical compounds using PubChem data 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. It currently returns a placeholder message indicating that 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) }] };
    }
  • src/index.ts:527-538 (registration)
    Registration of the 'predict_admet_properties' tool in the ListToolsRequestSchema response, including the tool name, description, and input schema definition.
    {
      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: [],
      },
    },
  • Input schema definition for the 'predict_admet_properties' tool, specifying optional cid or smiles inputs.
    inputSchema: {
      type: 'object',
      properties: {
        cid: { type: ['number', 'string'], description: 'PubChem Compound ID (CID)' },
        smiles: { type: 'string', description: 'SMILES string (alternative to CID)' },
      },
      required: [],
    },
  • Dispatcher case in the main CallToolRequestSchema handler that routes to the specific 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 properties but doesn't mention what kind of prediction it performs (e.g., machine learning model, rule-based), whether it requires authentication, rate limits, or what the output format might be. 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 extremely concise—a single sentence that efficiently conveys the core purpose without any wasted words. It's front-loaded with the essential information, making it easy for an agent to parse quickly. Every part of the sentence earns its place by defining the tool's function.

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 lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the prediction entails (e.g., numerical values, classifications), potential limitations, or how to interpret results. For a tool with significant functional scope and no structured output documentation, more context is needed.

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 the schema already documents both parameters (cid and smiles) with clear descriptions. The description doesn't add any additional meaning about the parameters beyond what's in the schema, such as explaining the relationship between them or providing usage examples. With high schema coverage, 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: 'Predict ADMET properties (Absorption, Distribution, Metabolism, Excretion, Toxicity)'. It specifies the verb ('Predict') and the resource ('ADMET properties'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_toxicity_info' or 'assess_drug_likeness', which might overlap in scope.

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 many sibling tools available (like 'get_toxicity_info', 'assess_drug_likeness', 'get_compound_properties'), there's no indication of what makes this tool distinct or when it's the appropriate choice. The lack of context leaves the agent without usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/k-lordbodin7/PubChem-MCP-Server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server