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Glama

Data Montreal

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

A4.3/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses several useful behaviors: the tool fans out across multiple sources in parallel, returns sources_used/sources_failed, treats empty sections as real 'no data', soft-fails on the USPTO PatentsView API sunset, and explains that a company with only small-molecule drugs showing no fda_products is expected. This is exactly the kind of context annotations cannot convey.

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 content is dense and genuinely useful, but it is delivered as one large run-on paragraph using semicolons to separate major return sections. While every clause earns its place given the tool's complexity, the lack of bullets or clear structural breaks makes it harder to parse than it should be. It is adequate but not a model of concise organization.

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 present, the description carries the full burden of explaining return values, and it does so thoroughly: cik, resolved_to/resolved_from, recent_filings with URI examples, fundamentals (10-K fields and sorting), patents, federal_contracts, fda_products, hiring, news, LEI, and sources_used/sources_failed. It also covers edge cases like private companies and soft-fail behavior, so an agent has everything needed to invoke and interpret the result.

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%, so the schema already documents both `type` and `value` thoroughly. The description mostly restates the same information ('Ticker, CIK, or company name', 'both are accepted... interchangeably'), adding examples but no new semantic detail. This matches the calibrated baseline of 3 for high schema coverage.

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 battery of natural-language examples ('Tell me about X', 'research Acme') followed by a crisp statement: 'full cross-source profile of a US public company in ONE parallel call.' This gives a specific verb-like action (profile) and a bounded resource (US public company), and it explicitly contrasts with 'chaining single-pack SEC/XBRL/news lookups,' so an agent can tell it apart from narrower lookup tools.

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 says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which clearly states when to use this tool vs. an alternative pattern. It also covers the private-company case (returns resolved:false with a notes line). However, it does not explicitly exclude any sibling tools such as compare_entities or deep_research, so the when-not-to-use guidance is slightly 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

A4/5.0
Disambiguation3/5

Most tools have distinct purposes with detailed descriptions, but ask_pipeworx_beta is explicitly identical to ask_pipeworx, and the several polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage) have overlapping prediction-market territory. Descriptions help differentiate, but the overlaps could still cause misselection.

Naming Consistency4/5

The majority follow a clear verb_noun snake_case pattern (e.g., compare_entities, resolve_entity, generate_llms_txt). A few tools deviate with noun/adjective prefixes (montreal_datasets, montreal_recent, pipeworx_feedback, recent_alerts) or single verbs (remember, recall, forget), but the overall style is consistent and readable.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold and feels heavy. While each tool has a distinct role, the sheer number—spanning data access, prediction markets, memory, subscriptions, and meta-tools—makes the surface harder for agents to navigate compared to a more focused server.

Completeness4/5

For its broad data-gateway purpose, the server covers a wide range: lookups, research, entity resolution, claim validation, memory, subscriptions, and feedback. The Montreal-specific subset (datasets, query, recent) is adequate for the apparent scope, with only minor gaps like no subscription-update tool.

Resources