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Glama

Dbnomics

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, open-world, non-destructive behavior. The description adds substantial behavioral detail beyond that: source fan-out, soft-fail behavior for USPTO PatentsView, sources_used/sources_failed semantics, empty sections meaning no data not a bug, and private-company resolution behavior. There is no contradiction with 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 long, but every sentence carries meaningful information about a genuinely complex tool. It is front-loaded with example queries and the key 'ALWAYS PREFER' guidance, and it organizes output sections and failure semantics efficiently. Slightly dense and run-on, but the length is justified by the tool's complexity.

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 carries the full burden of explaining return values, and it does so thoroughly: each output section is listed with its semantics, edge cases are explicitly handled, and source success/failure reporting is explained. For a tool with this much fan-out complexity, the description is effectively complete.

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 schema already documents both parameters. The description adds value by reinforcing that type accepts 'company' or 'ticker' interchangeably and that value can be a ticker, CIK, or company name, with name resolution via SEC EDGAR. This is helpful beyond the schema, though not dramatically novel.

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 clearly identifies the tool as producing a full cross-source profile of a US public company, with concrete example queries and a list of return sections. It distinguishes itself from single-pack SEC/XBRL/news lookups by explicitly stating it fans out across many sources in one parallel call.

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?

The description gives explicit when-to-use guidance: prefer this over chaining single-pack lookups when the user wants a holistic view. It also covers important edge cases such as private companies returning resolved:false and type/value interchangeability, leaving little ambiguity about selection or invocation.

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

A3.6/5.0
Disambiguation2/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, while bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities. The detailed descriptions mitigate some confusion, but an agent must read carefully to avoid selecting the wrong member of these overlapping groups.

Naming Consistency3/5

The set is consistently snake_case and many tools follow verb_noun conventions like list_datasets, get_series, find_series, and validate_claim. However, a large minority are noun-led names such as entity_profile, deep_research, bet_research, pipeworx_feedback, and polymarket_arbitrage, so there is no single predictable naming pattern across the whole server.

Tool Count2/5

At 36 tools, this is well above the 25+ threshold for an over-heavy surface, and the set spans DBnomics data, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI-visibility checks. Several near-duplicate meta-tools could be consolidated, and unrelated domains would be better split into separate servers.

Completeness4/5

Core workflows are well covered: DBnomics browse/fetch/search, company resolve/profile/compare/change, prediction-market discovery and fill-risk, and memory/subscription lifecycles are all represented. Minor gaps exist, such as no subscription-update operation and no direct single-dataset detail fetch without listing, but agents can work around them.

Resources