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Opentargets

by io.github.pipeworx-io

Entity Profile

entity_profile
Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context beyond this: it fans out across specific sources, soft-fails when USPTO is unreachable, uses GDELT→GNews fallback, returns up to 5 filings with URIs, and sorts fundamentals by period_end descending. This explains runtime behavior and failure modes.

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 appropriately sized for the tool's complexity. It front-loads user-facing examples, then systematically lists outputs, sources, fallbacks, and input limitations. Every sentence contributes actionable information 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?

Despite no output schema, the description fully enumerates return fields and their sources. It covers prerequisites (ticker/CIK), limitations (names unsupported, patent API sunset), error behavior (soft-fail), and suggests a sibling tool (resolve_entity). For a multi-source profile tool, this is complete.

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?

Schema coverage is 100% and both parameters are described in the schema. The description reinforces value formatting with examples ('AAPL', '0000320193') and repeats the constraint that names are unsupported, adding slight value beyond the schema without introducing new semantic details.

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 purpose: 'full cross-source profile of a US public company in ONE parallel call.' It uses a specific verb ('profile') and resource ('US public company'), distinguishes from siblings like compare_entities and deep_research, and lists concrete outputs (cik, filings, fundamentals, patents, news, LEI).

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?

Explicit guidance is provided: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also gives clear exclusions: 'names not supported (use resolve_entity first if you only have a name)' and specifies accepted input formats (ticker or zero-padded CIK).

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