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Opentargets

by io.github.pipeworx-io

Target Known Drugs

target_known_drugs
Read-onlyIdempotent

"What drugs target [gene]" / "approved drugs against [target]" / "clinical-trial drugs for [gene]" — drugs that have been clinically tested or approved against a drug target (Ensembl gene ID). Returns drug names, mechanisms, indications, clinical trial phases. Use for competitive landscape / drug-repositioning queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNo1-100 (default 25)
ensembl_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds scope context ('clinically tested or approved') and output details, but these are more about data scope than behavioral traits like rate limits or side effects. It does not contradict annotations.

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 concise yet packs valuable information: example queries, scope, output fields, and use case. Every sentence adds value, and the structure is front-loaded with examples.

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

Completeness5/5

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

For a query tool with one required parameter and an output schema, the description provides sufficient context. It explains the purpose, the data scope, and the intended use cases, making it complete for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers size with a description, but ensembl_id lacks any description. The clarification that the target is an 'Ensembl gene ID' compensates for the missing schema description, improving parameter understanding. Size is already documented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: identifying drugs that have been clinically tested or approved against a given Ensembl gene ID. It explicitly lists the output content (drug names, mechanisms, indications, phases) and provides example queries. This distinguishes it from sibling tools like target and target_associations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit usage context: 'Use for competitive landscape / drug-repositioning queries.' This is clear guidance on when to use the tool, but it does not mention when not to use it or suggest alternatives, so it falls short of a 5.

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

All Opentargets toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources