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Producthunt

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

A5/5.0
Behavior5/5

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

The description goes far beyond the readOnly/openWorld/idempotent annotations by explaining the fan-out across many data sources, the sources_used/sources_failed mechanism, and that an empty section means real 'no data,' not a bug. It also discloses name resolution behavior and the expected behavior for small-molecule/generic-only companies.

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 long but information-dense, with user-intent triggers front-loaded and no filler. Every sentence earns its place by conveying source details, return semantics, or important edge-case behavior that would otherwise be absent without an output schema.

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?

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: CIK, company name, resolution fields, filings, fundamentals, patents, contracts, FDA products, hiring, news, LEI, and source success/failure tracking. It is complete enough for an agent to understand what the tool returns and how to interpret it.

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

Parameters5/5

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

Although the schema already covers both parameters fully, the description adds meaningful semantics: accepted value shapes (ticker, zero-padded CIK, or name), type interchangeability, resolved_from/resolved_to behavior, and the private-company failure mode. These details help an agent construct correct calls.

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 a specific verb and resource: it produces a full cross-source profile of a US public company in one call. It also distinguishes itself from an alternative approach ('chaining single-pack SEC/XBRL/news lookups') and from related use cases by noting 'person/place coming soon.'

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?

It explicitly says to prefer this tool when the user asks for a holistic view or says 'tell me about X,' and to prefer it over chaining multiple lookups. It also describes the when-not case: private companies resolve as false with an explicit notes line, not a bare failure.

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

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TDQS

A3.8/5.0
Disambiguation2/5

Several tools are functionally near-identical: ask_pipeworx_beta is explicitly a duplicate of ask_pipeworx (descriptions say they 'currently match exactly'), and ask_pipeworx_grounded differs only in answer-extraction mode. The six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap on edge-finding and fill-risk, and ai_visibility_check duplicates scan_competitor_ai_presence's per-entity probing. Agents will frequently misselect among these.

Naming Consistency4/5

Naming is consistently snake_case with a mostly verb_noun pattern (get_post, top_launches, subscribe, forget, validate_claim, resolve_entity). Minor deviations exist where nouns lead (entity_profile, recent_changes, recent_alerts, bet_research), and polymarket_* names use a domain-prefix style rather than pure verb_noun, but the overall pattern is predictable and readable.

Tool Count2/5

33 tools is at the heavy end, and the count is badly mismatched to the server's stated identity: it is named 'Producthunt' yet only 2 of 33 tools (get_post, top_launches) are Product Hunt related — the rest are Pipeworx data lookup, prediction-market, memory, and subscription tools. The redundancy (duplicate ask_pipeworx_beta, overlapping polymarket tools) inflates the count without adding surface.

Completeness2/5

Judged against the Product Hunt domain implied by the server name, the surface is severely incomplete: coverage is limited to list-top-launches and get-one-post, with no search, users, comments, votes, collections, or categories — and no way to act on Product Hunt data at all. As a general Pipeworx data platform the coverage is broader, but for the named purpose there are large gaps that will force agent failures.

Resources