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Could Have Been Email

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

A4.8/5.0
Behavior5/5

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

Even though annotations already declare readOnlyHint/openWorldHint/idempotentHint/destructiveHint, the description adds substantial behavioral detail: cross-source fan-out, empty-section semantics, sources_used/sources_failed, FDA 'none is expected', USPTO soft-fail, and GDELT→GNews fallback. 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.

Conciseness4/5

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

The description is long, but the tool is complex and there is no output schema, so the density is largely justified. It is front-loaded with trigger examples and the key 'ALWAYS PREFER' guidance. However, the semicolon-heavy wall-of-text formatting slightly hurts scannability.

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 data, and it does so thoroughly: required output fields, URIs, fallbacks, expected-empty cases, and input-resolution behavior. An agent has enough context to select, invoke, and interpret the result 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?

Schema coverage is 100%, so the baseline is 3, but the description goes further by explicitly stating that `type` values are interchangeable and that `value` may be a ticker, zero-padded CIK, or company name with same behavior. It also adds meaningful edge-case behavior for private companies.

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 resource and action: building a full cross-source profile of a US public company. It opens with concrete trigger phrases and explicitly separates this holistic tool from single-pack SEC/XBRL/news lookups, so an agent can distinguish it from narrower sibling tools.

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 gives explicit guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also covers edge cases like private companies returning resolved:false and accepted input forms, making the selection and invocation context clear.

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

A3.9/5.0
Disambiguation2/5

Several tool clusters are nearly indistinguishable in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and the six polymarket tools heavily overlap in surfacing prediction-market edge. Even with detailed descriptions, an agent could easily misselect between bet_research and polymarket_edges or between discover_tools and suggest_questions.

Naming Consistency3/5

Most names use lowercase snake_case, but the pattern is mixed: some are verb_noun (compare_entities, resolve_entity), some are bare verbs (remember, forget, recall), and some are compound noun phrases (polymarket_edges, pipeworx_trending). ask_pipeworx also breaks the separator convention compared to ask_pipeworx_beta and ask_pipeworx_grounded.

Tool Count2/5

With 32 tools, this exceeds the 25+ threshold for 'too many' and feels like a platform bundle rather than a focused server. It spans data querying, prediction markets, memory, subscriptions, feedback, AI visibility, dependency scanning, and llms.txt generation, which is far more surface area than one coherent server should present.

Completeness4/5

For the core data-research and prediction-market domains, coverage is strong: query, grounded verification, deep research, entity resolution, comparisons, change feeds, arbitrage, fill-risk, subscriptions, and memory are all present with no major dead ends. The gaps are mostly the single-purpose oddballs (could_have_been_email_analyze, generate_llms_txt, scan_dependency) that don't connect to the rest of the surface.

Resources