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Augmented-Nature

ChEMBL MCP Server

get_drug_info

Retrieve drug development status and clinical trial data for a specific compound using its ChEMBL ID.

Instructions

Get drug development status and clinical trial information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chembl_idYesChEMBL compound ID

Implementation Reference

  • Main handler function for 'get_drug_info' tool. Validates input, fetches molecule details and drug indications from ChEMBL API, returns formatted JSON response.
    private async handleGetDrugInfo(args: any) {
      if (!isValidChemblIdArgs(args)) {
        throw new McpError(ErrorCode.InvalidParams, 'Invalid drug info arguments');
      }
    
      try {
        // Get molecule information
        const moleculeResponse = await this.apiClient.get(`/molecule/${args.chembl_id}.json`);
        const molecule = moleculeResponse.data;
    
        // Get drug indication data if available
        let indications = [];
        try {
          const indicationResponse = await this.apiClient.get('/drug_indication.json', {
            params: { molecule_chembl_id: args.chembl_id, limit: 50 },
          });
          indications = indicationResponse.data.drug_indications || [];
        } catch (e) {
          // Indications may not be available for all compounds
        }
    
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify({
                chembl_id: args.chembl_id,
                molecule_info: molecule,
                development_phase: molecule.max_phase,
                indications: indications,
              }, null, 2),
            },
          ],
        };
      } catch (error) {
        throw new McpError(
          ErrorCode.InternalError,
          `Failed to get drug info: ${error instanceof Error ? error.message : 'Unknown error'}`
        );
      }
    }
  • src/index.ts:778-781 (registration)
    Registration of 'get_drug_info' tool handler in the CallToolRequestSchema switch statement.
    case 'search_drugs':
      return await this.handleSearchDrugs(args);
    case 'get_drug_info':
      return await this.handleGetDrugInfo(args);
  • Tool schema definition and registration in ListToolsRequestSchema response, including input schema requiring 'chembl_id'.
    name: 'get_drug_info',
    description: 'Get drug development status and clinical trial information',
    inputSchema: {
      type: 'object',
      properties: {
        chembl_id: { type: 'string', description: 'ChEMBL compound ID' },
      },
      required: ['chembl_id'],
    },
  • Helper validation function isValidChemblIdArgs used to validate input arguments for get_drug_info and similar tools.
    const isValidChemblIdArgs = (
      args: any
    ): args is { chembl_id: string } => {
      return (
        typeof args === 'object' &&
        args !== null &&
        typeof args.chembl_id === 'string' &&
        args.chembl_id.length > 0
      );
    };
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 what information is retrieved but doesn't cover aspects like rate limits, authentication needs, error handling, or the format/scope of returned data (e.g., pagination, completeness). This leaves significant gaps for an AI agent to understand how the tool behaves.

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 directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy for an AI agent to parse quickly.

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

Completeness3/5

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

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the core purpose but lacks details on usage context, behavioral traits, and output expectations, which could hinder effective tool 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?

The input schema has 100% description coverage, with the single parameter 'chembl_id' clearly documented as 'ChEMBL compound ID'. The description doesn't add any extra meaning beyond this, such as examples or constraints, but the schema provides adequate baseline 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 with specific verbs ('Get') and resources ('drug development status and clinical trial information'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_compound_info' or 'search_drugs', which might also provide related drug information, so it doesn't reach the highest score.

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 (e.g., 'get_compound_info', 'search_drugs'), there is no indication of context, prerequisites, or exclusions to help an AI agent choose appropriately.

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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