disease"Disease profile for [EFO_N]" / "look up disease [ID]" — fetch a disease profile from Open Targets by EFO (Experimental Factor Ontology) ID. Returns name, description, therapeutic areas, ontology cross-refs. Pair with disease_associations to find drug targets for the disease.
| Name | Required | Description | Default |
|---|---|---|---|
| efo_id | Yes | e.g. "EFO_0000270" (asthma) |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, open-world, and non-destructive. The description adds behavioral context by naming the data source (Open Targets) and the specific fields returned, which goes beyond the annotation safety profile. It also hints at the ontology-driven nature, giving the agent a clear mental model. No contradiction exists with annotations.
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 concise and front-loaded with command examples. Every clause contributes: it conveys the purpose, the data source, the parameter, the return fields, and a complementary tool in just two sentences. There is no wasted text or redundancy.
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 tool with one required parameter, an output schema, and comprehensive annotations, the description is complete. It states what the tool returns, how to invoke it, and how it connects to a sibling tool. The presence of an output schema means return values need no additional explanation, making the description fully sufficient for effective use.
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 input schema already documents `efo_id` with an example, providing 100% coverage. The description then enriches the parameter by explaining that EFO stands for Experimental Factor Ontology and by showing the parameter in command templates. This adds beyond the schema's basic example, making it clearer for agents.
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 a specific action: fetching a disease profile from Open Targets by EFO ID. It lists the return fields (name, description, therapeutic areas, ontology cross-refs), and the command patterns ('Disease profile for [EFO_N]' / 'look up disease [ID]') reinforce the purpose. It also differentiates from sibling tools like disease_associations by positioning itself as the profile lookup complement.
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?
It explicitly instructs to pair with `disease_associations` to find drug targets, which both differentiates the tool and gives practical guidance. The command patterns also illustrate invocation. However, it does not explicitly mention exclusions or compare with other siblings like `entity_profile` or `resolve_entity`, though the pairing note provides strong contextual guidance.
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.