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Glama

Sanctions Screening

by io.github.pipeworx-io

Deep Research

deep_research
Read-onlyIdempotent

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1506 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,767 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a hop field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).
questionYesThe research question, in natural language. Broad/multi-part is fine — decomposition is the point.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."
  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 declare readOnly/openWorld/idempotent, and the description goes well beyond them by disclosing account requirements, paid-dependency for 'thorough', latency expectations, parallel decomposition, gap reporting, citation fetchability, and contradiction detection. There is no contradiction with the annotations; the description adds substantial behavioral context.

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 distinguishing information, and the account-required warning is front-loaded. The density is justified by the tool's complexity, though a little more structural separation would improve scannability.

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 full burden of explaining return values; it does so by describing the findings packet, evidence, confidence, source, fetched_at, citations, gaps[], contradictions[], and hop fields. It also covers auth, cost, latency, alternatives, and edge behavior for large records. This is remarkably complete for a complex research tool.

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 meaningful depth-level behavior beyond the enum labels, explaining gap-recovery hops, lead-chasing, and contradictions for standard/thorough. It does not add much for 'question', but that parameter is self-explanatory.

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 names a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1506 STRUCTURED data sources' delivered 'in ONE call.' It explicitly contrasts itself with open-web search and identifies its best use case: broad/multi-part structured-data questions. It is clearly distinguishable from sibling tools like ask_pipeworx.

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: use it for broad/multi-part structured research, and for single lookups use ask_pipeworx instead. It also names alternatives for breaking/current-news topics and warns that unsigned-in users should use ask_pipeworx. This is model-grade routing information.

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.

Resources