Fintech Intel

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

Name	Required	Description	Default
`type`	Yes	"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
`value`	Yes	Ticker (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.

Changed3 schema fields changedSep 3, 2026

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."

AddedMay 12, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already mark it read-only, open-world, idempotent, and non-destructive. The description goes well beyond, detailing the fan-out across SEC, XBRL, USPTO, USAspending, FDA Purple Book, DOL LCA, GDELT/GNews, and GLEIF, and specifying what each source contributes. It discloses important quirks: patents soft-fail after API sunset, FDA products only include licensed biologics (small-molecule/generic drugs yield none), and sources_used/sources_failed indicate what actually returned data. This level of transparency prevents false expectations.

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, exceeding 200 words, but every sentence carries information relevant to usage, output, or caveats. It uses examples, semicolons, and parentheticals to pack detail compactly. The structure front-loads the core purpose and usage guidance, then flows into source breakdown and return fields. While not brief, its density justifies the length; a slightly tighter edit could earn a 5.

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 must fully specify return values, and it does: it enumerates the returned fields (cik, company_name, recent_filings with URIs, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) and explains the resolved_from/to and sources_used/sources_failed metadata. It also covers expected empty-section behavior and the private-company failure mode. Given the tool's complexity across multiple data sources, this description leaves no obvious gap an agent would need to fill.

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%, but the description adds significant practical detail beyond the schema. It clarifies that 'type' accepts both 'company' and 'ticker' interchangeably, and that 'value' can be a ticker, zero-padded CIK, or company name with name resolution via SEC EDGAR. It also explains the edge case of a private company returning resolved:false. The description fully compensates and enriches parameter understanding beyond the schema.

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 specific user queries and states the exact action: return a full cross-source profile of a US public company in one parallel call. It explicitly distinguishes itself from single-pack SEC/XBRL/news lookups, and the phrase 'ALWAYS PREFER over chaining' shows clear differentiation from sibling tools like deep_research or resolve_entity. The resource (US public company) and verb (profile) are unambiguous.

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 a direct condition for use: when the user asks for a holistic view of a company, prefer this over chaining individual lookups. It also clarifies expected behavior for private companies (resolved:false with notes) and that empty sections indicate real absence of data, not errors. It explicitly notes person/place are not yet supported, which prevents misuse. This is prescriptive and context-aware.

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

A3.7/5.0

Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, discover_tools/suggest_questions/pipeworx_trending all serve discovery, and multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) find opportunities. Descriptions help but an agent could easily pick the wrong data-query tool.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern, mostly verb_noun (ask_pipeworx, resolve_entity, validate_claim) and prefix groups (fintech_*, polymarket_*). Minor deviations like entity_profile or recent_changes are noun phrases but still readable and predictable.

Tool Count2/5

34 tools is excessive for a coherent set; the server appears to bundle a general-purpose data research platform, prediction-market analysis, memory, and subscriptions under one 'Fintech Intel' name. Many meta-tools could be split into separate servers, and the count burdens an agent with unnecessary choices.

Completeness4/5

The tool surface covers the fintech/data-intel domain thoroughly: SEC filings, FDIC, FDA, economic data, real estate, prediction markets, monitoring subscriptions, and memory. Gaps are minor—e.g., no direct tool for historical stock charts, but the router (ask_pipeworx) handles such queries.

Resources

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