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Glama

Statcan

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

A4.5/5.0
Behavior5/5

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

Annotations already mark the operation read-only, idempotent, and non-destructive, so the description correctly does not reassert them. It adds substantial behavioral context beyond those hints: fan-out to multiple registries, empty sections are real 'no data' rather than bugs, sources_used/sources_failed, and resolved:false with a notes line for private companies.

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 front-loaded with purpose and examples, but the body is one dense, run-on paragraph with many semicolon-separated source details and parentheticals. All information is relevant for the tool's complexity, but the lack of paragraph/section structure and the long clauses make it harder to scan than is ideal.

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?

There is no output schema, so the description carries the burden of explaining return behavior; it does so by enumerating returned sections (cik, recent_filings, fundamentals, patents, etc.) and handling of failures like empty sections, private companies, and name resolution. The tool's complexity is fully covered for selection and invocation purposes.

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 `type` and `value` are already documented in the input schema, so the description is not required to explain them. It adds examples and repeats the interchangeability of 'company' and 'ticker' that the schema already states, providing no material semantic increment over the structured fields.

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 user phrases ('Tell me about X', 'brief me on Tesla') and a specific verb-resource pair: full cross-source profile of a US public company in one parallel call. It distinguishes from single-pack SEC/XBRL/news lookups and lists specific output domains, so an agent can identify it as the holistic profile tool rather than a point lookup.

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 contains an explicit routing rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies accepted input shapes and the behavior for private companies, which tells the agent when the tool is appropriate and what to expect when it isn't.

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
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, ask_pipeworx_grounded is the same router with an extra extraction pass, and deep_research/ask_pipeworx overlap for broad questions. The prediction-market tools and the StatCan series/cube/indicator tools also have fuzzy boundaries despite their detailed descriptions.

Naming Consistency3/5

Names consistently use snake_case, but the set mixes verb-first names (resolve_entity, validate_claim, subscribe) with domain-prefixed noun-first names (statcan_*, polymarket_*, pipeworx_*) and one-off names like ai_visibility_check and generate_llms_txt. The domain prefixes help navigation, but there is no single predictable pattern and the ask_pipeworx_* suffix variants break the prefix convention.

Tool Count2/5

38 tools is well beyond the 25+ threshold, and the server named Statcan carries only 8 StatCan-specific tools alongside general Pipeworx routing, prediction-market analysis, AI visibility, dependency scanning, memory, and subscription features. This feels like several servers merged into one rather than a well-scoped StatCan interface.

Completeness4/5

Within the apparent StatCan data-access scope, the surface is solid: listing cubes, metadata, cube data, vector series, headline indicators, CSV URLs, and change detection cover the core workflows. The broader Pipeworx/analysis layers also include discovery, grounded lookups, entity resolution, validation, subscriptions, and memory, with only minor gaps like server-side StatCan search and no way to execute on prediction-market signals.

Resources