drug"Drug info for [ChEMBL ID]" / "look up [drug] target info" / "[CHEMBL...] mechanism" — fetch a drug profile from Open Targets by ChEMBL ID. Returns name, mechanisms of action, indications, target genes, trade names, clinical-trial phase. Use for drug research, mechanism queries.
| Name | Required | Description | Default |
|---|---|---|---|
| chembl_id | Yes | e.g. "CHEMBL1201583" (imatinib) |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint: false, so the safety profile is covered. The description adds context by specifying the data source (Open Targets) and the exact return fields, but it does not disclose rate limits, auth requirements, or any subtle behaviors. This adds some value but not rich behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact at two sentences, but the opening alternates three phrasings ('Drug info for [ChEMBL ID]' / 'look up [drug] target info' / '[CHEMBL...] mechanism') which adds redundant phrasing. Still, information is front-loaded and every sentence carries purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read-only lookup tool, the description covers what it does, what it returns, and when to use it. An output schema exists to define the return structure, so the description does not need to explain that in detail. The tool is fully contextualized.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage for the single parameter chembl_id, including type and example values. The description merely reinforces that the tool expects a ChEMBL ID but adds no extra syntactic meaning beyond the schema. Baseline of 3 applies due to high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a drug profile from Open Targets by ChEMBL ID and lists the returned fields (name, mechanisms, indications, target genes, trade names, clinical-trial phase). This specific verb+resource distinction separates it from sibling tools like disease or target.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage context: 'Use for drug research, mechanism queries.' This indicates when to invoke the tool, though it does not explicitly name alternatives or exclusions. It offers more guidance than a bare lookup but stops short of comparative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several clusters of tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer data questions; entity_profile, compare_entities, and recent_changes all pull company data; polymarket_edges, polymarket_arbitrage, and bet_research all analyze prediction markets. Though descriptions are detailed, the boundaries are subtle and the beta variant is nearly identical to the stable one.
Most names are snake_case, but there's no consistent verb_noun pattern: some are bare nouns (disease, target, drug, search), some are verb phrases (resolve_entity, validate_claim, generate_llms_txt), and some are domain-prefixed (ask_pipeworx_*, polymarket_*, target_*). The mixed conventions make it hard to predict tool names.
38 tools is far beyond the typical well-scoped server, and the set mixes Open Targets lookup, a general data platform (Pipeworx), prediction markets, npm package checks, and AI-marketing utilities under the name 'Opentargets'. Many tools are unrelated to the server's apparent core purpose.
The Open Targets drug-discovery workflow is well covered: search for IDs, get disease/drug/target profiles, and get associations/known drugs. However, the broader platform lacks some lifecycle operations (no create/update/delete since it's read-only), and the unrelated utilities (generate_llms_txt, scan_dependency) appear tacked on rather than filling domain gaps.