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Glama

Sanctions Screening

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 indicate read-only, open-world, idempotent, non-destructive. The description adds substantial behavioral context: it names all data sources, explains the return structure, clarifies that empty sections mean 'no data' not a bug, notes the USPTO API sunset and soft-fail, and mentions name resolution via SEC EDGAR. This goes well beyond the annotations, giving the agent a complete picture of what to expect.

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 densely packed with necessary facts. It leads with examples and a directive, then systematically covers sources, return fields, and edge cases. It could be broken into bullets for easier scanning, but every sentence contributes value and nothing is verbose for its own sake.

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 sources, no output schema), the description is exceptionally complete. It explains all expected outputs, the meaning of empty sections, resolution failures, and source fallbacks. An agent has everything needed to call it correctly and interpret results.

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% and both parameters are described. The description adds examples and clarifies that 'company' and 'ticker' are interchangeable and accept the same value shapes, and it explains the format of `value` (ticker, CIK, name). This is extra meaning beyond the schema, so it earns a 4.

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 states it is a full cross-source profile of a US public company, with multiple query examples. It explicitly lists the sources and return fields, and it differentiates from siblings like deep_research and compare_entities. The purpose is unambiguous and specific.

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 explicitly says 'ALWAYS PREFER over chaining single-pack looksups when the user asks for a holistic view,' providing clear when-to-use guidance. It also distinguishes between public and private companies and mentions optional behavior for names vs tickers. Though it does not list explicit exclusions, the guidance is strong and actionable.

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

Many tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are nearly identical, several prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) cover similar territory, and research/verification tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) blur together. The three sanctions tools are distinct, but the rest of the set creates frequent misselection risk.

Naming Consistency3/5

All tool names use snake_case, which is consistent, but the verb/noun ordering varies unpredictably (expectation vs entity_profile vs sanctions_screen vs scan_dependency). There is no dominant pattern like verb_noun, though names are readable.

Tool Count2/5

With 34 tools, the server is over-scoped, especially given its narrow 'Sanctions Screening' title. Many tools are duplicative (ask_pipeworx_beta duplicates ask_pipeworx; scan_competitor_ai_presence wraps ai_visibility_check), and the bulk are unrelated to the stated purpose. The count feels bloated.

Completeness3/5

The three sanctions tools (screen, entry, lists) cover the core read-only workflow well, but the server is mislabeled: most of the 34 tools are generic data/Pipeworx features unrelated to sanctions. For the broad data domain the set is fairly complete, but for the apparent 'Sanctions Screening' purpose it is both over- and under-scoped, with no bulk screening or list management.

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