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Opentargets

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

TDQS

A4.7/5.0
Behavior5/5

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

Adds significant context beyond annotations: key-value storage scoped by identifier, persistence differences for authenticated vs anonymous sessions (24 hours), and cross-session retention. These details are not in annotations and are crucial for safe use. No contradiction 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loads the core action, and every sentence contributes value—usage, behavior, retention policy, and related tools without redundancy.

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?

Given the simple two-parameter schema and annotations, the description fully covers the tool's purpose, persistence behavior, and relationship to sibling tools. No output schema needed for a store operation.

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 already provides 100% parameter descriptions, so the baseline is 3. The description reinforces key-value semantics and offers examples in the usage guidance, but does not add substantial new information about the parameters themselves.

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 saves data for later reuse using the verb 'save' and specifies the resource (data/memory). It distinguishes itself from siblings by explicitly mentioning 'recall' for retrieval and 'forget' for deletion, aligning with the sibling tool names.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance: 'Use when you discover something worth carrying forward' and lists examples like resolved ticker or user preference. It also names alternatives: 'Pair with recall to retrieve later, forget to delete,' giving clear context.

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