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hackernews

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

A4.6/5.0
Behavior5/5

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

The description discloses significant behavior beyond the annotations: it fans out in parallel, returns specific fields like recent_filings with pipeworx URIs, and notes that the USPTO PatentsView API sunset in May 2025 and soft-fails. It also explains the fallback chain for news (GDELT→GNews) and the LEI via GLEIF. These details are not present in the annotations and provide valuable behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is dense but not overly long, starting with relatable example queries then moving to technical details. It uses semicolons to list return fields, making it scannable. However, it's a single long run-on sentence; breaking it into bullet points or shorter sentences would improve readability. Still, every sentence earns its place.

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?

Without an output schema, the description carries the burden of explaining return values, and it does so thoroughly: it lists each returned field (cik, company_name, recent_filings, fundamentals, patents, news, LEI) and clarifies edge cases like the patents API soft-fail and the name limitation. Given the tool's complexity (multi-source fan-out), the description is complete enough for an agent to understand what it will receive and how to use it correctly.

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 description coverage is 100%: both parameters (type and value) are fully described with enums and examples. The description essentially repeats what's in the schema, adding the example queries but no new parameter semantics. Baseline 3 is appropriate because the schema does the heavy lifting.

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: 'full cross-source profile of a US public company in ONE parallel call.' It also distinguishes itself from siblings by noting it fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns a structured set of fields. Example queries ('tell me about X', 'research Acme') provide concrete usage context, and the mention of 'ALWAYS PREFER' separates it from chaining single-pack lookups.

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 tells when not to use it directly: 'names not supported (use resolve_entity first if you only have a name).' This gives clear direction on both preferred and fallback usage.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, and deep_research also overlaps with them. The Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have fuzzy boundaries. An agent could easily pick the wrong meta-tool or duplicate functionality.

Naming Consistency3/5

All names are snake_case, but the pattern is mixed: some are verb_noun (get_item, list_subscriptions, resolve_entity), some are noun_noun (entity_profile, pipeworx_feedback, polymarket_edges), some are adjective_noun (deep_research, recent_alerts), and a few are single verbs (forget, recall, remember, subscribe). This is readable but not a consistent convention.

Tool Count2/5

With 36 tools, this is far too many for a server named 'hackernews'. The bulk of the tools concern Pipeworx data research, prediction markets, memory, and subscriptions — unrelated to the server's apparent purpose. Many of these could be split into separate servers, and the HN-specific functionality would be better served by a focused set of ~5-8 tools.

Completeness2/5

For the Hacker News domain implied by the server name, the surface is incomplete: there are read-only tools (search, top stories, item/comments) but no write functionality (submit, comment, vote) and no user profile access. The broader data-research capabilities are fairly comprehensive, but that does not rescue the server's coherence given its stated name.

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