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Glama

Uk Ons

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description goes well beyond this by disclosing important behavioral traits: the patent source soft-fails after May 2025, empty sections (like fda_products for small-molecule-only companies) are expected rather than bugs, private companies return resolved:false with an explicit notes line, and sources_used/sources_failed indicate real data absence. This richly supplements the annotation coverage and adds no contradictions.

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 every sentence carries actionable information, covering input forms, output fields, failure modes, and edge cases. The core purpose is front-loaded in the first sentence, then details expand logically. It could be slightly more structured (e.g., bullet points), but for a tool this complex, the density is justified and not wasteful.

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?

Given the tool's complexity (multiple data sources, many output fields, no output schema), the description is exceptionally complete. It enumerates all returned sections (cik, company_name, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI), explains the semantics of sources_used/sources_failed, covers edge cases (private companies, patent sunset), and describes the output URIs. Nothing an agent needs to call it correctly or interpret results is missing.

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?

Schema coverage is 100% and both parameters are described, but the description adds significant semantic value beyond the schema: it clarifies that 'type' accepts 'company' or 'ticker' interchangeably, that 'value' can be a ticker, CIK, or company name, and explains the resolution behavior for names (via SEC EDGAR company-name match). This goes beyond what the schema states, providing crucial disambiguation for callers.

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 a clear verb+resource: 'full cross-source profile of a US public company in ONE parallel call.' It immediately distinguishes itself from chaining single-pack lookups and from sibling tools like compare_entities, deep_research, and resolve_entity by emphasizing the holistic, one-call nature. The included example phrasings ('Tell me about X', 'research Acme') make the trigger conditions unmistakable.

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,' giving a clear when-to-use directive. It also notes name resolution behavior, which helps differentiate from resolve_entity. However, it doesn't explicitly name sibling tools as alternatives or state explicit 'do not use' conditions for specific cases, so it stops short of a 5 but is strong.

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
Disambiguation3/5

Most tools have clearly separated jobs, but the set is crowded with overlapping research/query entry points: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and the Polymarket/research cluster has fuzzy boundaries. Detailed descriptions help, but an agent can still easily mis-select among these tools.

Naming Consistency3/5

Names are consistently snake_case and readable, but they mix verb-led commands (get_dataset, list_editions, validate_claim) with noun-led descriptive names (entity_profile, polymarket_edges, recent_changes) and a version-suffixed duplicate (ask_pipeworx_beta). The ONS tools also lack a shared ons_ prefix aside from ons_timeseries, so the naming is more a collection of conventions than one predictable pattern.

Tool Count2/5

37 tools is well past the heavy range for a server whose name suggests a focused UK ONS statistics surface; only about six tools actually serve ONS data, while the rest span memory, subscriptions, prediction markets, npm scanning, AI visibility, and general Pipeworx plumbing. The count is not an extreme 50+ sprawl, but it is too many for a coherent scope.

Completeness4/5

The core ONS read workflow is well covered: catalog discovery through list_datasets, dataset/edition/version metadata, filtered get_observations, and classic time series via ons_timeseries. Minor gaps exist, such as no dedicated dataset search and some peripheral one-off features like scan_dependency or generate_llms_txt, but agents can work around them.

Resources