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Walkscore

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

A4.5/5.0
Behavior5/5

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

The description discloses substantial behavior beyond the annotations: it fans out across multiple sources in parallel, reports which sources actually returned data via sources_used/sources_failed, explains that an empty section is a real 'no data' rather than a bug, and details the private-company resolved:false path. These are meaningful non-obvious behaviors. No contradiction with the readOnly/openWorld/idempotent/non-destructive annotations is present.

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

Conciseness3/5

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

The description is dense and front-loaded with purpose, but it becomes a long run-on when listing sources and return fields, and it contains a garbled segment ('Cash, sorted?') that hurts readability. While every sentence largely earns its place, the structure is not clean or concise enough for a 4 or 5.

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 provided, the description carries the full burden of explaining return values, and it does so well: it lists key output fields (cik, company_name, recent_filings, fundamentals), includes example URIs, explains sources_used/sources_failed semantics, and covers edge cases like private companies and name resolution. This is enough for an agent to call the tool correctly.

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?

The input schema already covers both parameters thoroughly with 100% description coverage, including ticker/CIK/name examples and name resolution. The tool description adds extra context such as the private-company resolved:false behavior and the explicit 'type accepts company or ticker interchangeably' note, going somewhat beyond the schema. Since the schema carries most load, a 4 is appropriate.

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 opens with concrete example queries and immediately states the core purpose: 'full cross-source profile of a US public company in ONE parallel call.' This is a specific verb+resource with clear scope, and it distinguishes the tool from chaining single-pack SEC/XBRL/news lookups, so an agent can tell what it is for.

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

Usage Guidelines4/5

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

The description explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which is a clear when-to-use condition. It does not name sibling alternatives like deep_research or compare_entities, or state exclusions, so it stops short of a full 5.

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

A4.1/5.0
Disambiguation3/5

Tools are generally distinct but several clusters (ask_pipeworx family, polymarket family) have highly similar names that could cause confusion. For example, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all appear to do similar things with subtle differences. The agent would need to read descriptions carefully to pick the right one.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., compare_entities, resolve_entity, validate_claim). However, a few are single verbs (remember, recall, forget) or have inconsistent suffixes (ask_pipeworx_beta, walkscore_score). This is mostly consistent but not perfectly uniform.

Tool Count3/5

32 tools is quite high, bordering on excessive. While each tool serves a distinct purpose, the sheer number may overwhelm the agent. However, the tools cover a wide range of functionality, so the count is not unreasonable for a comprehensive data platform.

Completeness4/5

The tool set is comprehensive for querying data, conducting research, managing subscriptions, and evaluating bets. It includes meta-tools for discovery and memory. Notable gaps include user account management and direct file handling, but overall coverage is strong.

Resources