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Nass

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses substantial behavior: "USPTO PatentsView API sunset May 2025 — soft-fails until reactivated," the "GDELT→GNews fallback," and the semantic contract that "an empty section is a real 'no data', not a bug" via sources_used/sources_failed. It also pre-empts edge cases like private companies returning "resolved:false with an explicit notes line, not a bare failure." Nothing contradicts the 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 well front-loaded — the one-call cross-source promise and the ALWAYS PREFER rule appear in the opening sentences — and virtually every sentence carries a distinct fact, with no filler. The trade-off is a single dense paragraph of roughly 230 words packing sources, return fields, failure modes, and edge cases; structure would help, but the density is largely earned given there is no output schema to carry that burden.

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 and high tool complexity (eight-plus data sources, many return sections, multiple input forms), the description shoulders the full burden and delivers: it enumerates each return field with detail ("fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC)"), explains the sources_used/sources_failed reporting, and clarifies why a company may legitimately show no fda_products. An agent has what it needs to call, interpret, and troubleshoot this tool.

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 adds real value beyond the schema: concrete input examples ("AAPL", "0000320193", "Moderna"), the zero-padding requirement for CIKs, and the clarification that "company" and "ticker" are interchangeable and both accept all three value shapes. It also explains that names resolve via SEC EDGAR's company-name match, which the schema only implies.

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 seven concrete query examples then states the precise function: "full cross-source profile of a US public company in ONE parallel call." It names the exact resource scope (US public company) and explicitly contrasts itself with "chaining single-pack SEC/XBRL/news lookups," so an agent can distinguish it from narrower data tools among the siblings.

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?

Usage guidance is explicit and directive: "ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view," backed by a list of triggering phrasings. It does not, however, draw exclusion boundaries against semantically near siblings like compare_entities or deep_research, so the when-to-use guidance is clear but the when-not-to-use guidance is incomplete.

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
Disambiguation4/5

Most tools have clearly distinct purposes, especially the specialized ones like nass_crop_progress and bet_research. However, there is potential confusion between ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research, as they all involve querying structured data. The descriptions do attempt to differentiate them, but the overlap could still cause misselection.

Naming Consistency4/5

Tool names consistently use lowercase with underscores and follow patterns like descriptive prefixes (nass_, pipeworx_, polymarket_) and action verbs (generate_, resolve_, validate_). Minor inconsistencies exist, such as 'search_within' versus 'discover_tools', but overall the naming is predictable and clear.

Tool Count3/5

With 36 tools, the server is on the heavier side but still fits within a reasonable range for a comprehensive data platform. The tools cover a wide variety of domains (agriculture, finance, prediction markets, memory, etc.), and each tool appears to add value. However, the count is borderline high, and some tools like ask_pipeworx could potentially replace many specialized ones.

Completeness4/5

The tool surface is comprehensive for its purpose: answering questions over structured data with support for analysis, comparison, monitoring, and feedback. There are minor gaps, such as the lack of a direct update mechanism for subscriptions beyond canceling, but the core CRUD operations are covered. The inclusion of meta-tools like ask_pipeworx and deep_research fills many potential gaps.

Resources