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Glama

Sanctions Screening

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, and the description adds critical behavioral nuances: the meaning of each verdict, especially the crucial warning that 'could_not_verify' means the check did not happen and must not be treated as evidence. It also discloses the SEC fast path vs grounded pipeline behavior and the presence of verification_error{stage,detail}, going well beyond annotation data.

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?

A longer description but well-structured and front-loaded with trigger phrases and the primary use case. Every sentence contributes: the routing logic, return format, and the critical error-handling caveat. Not bloated, though it could be tightened; the 'Replaces 4–6 sequential calls' line is useful context for selection.

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 no output schema, the description fully specifies the return values (verdict enum, actual value with citation, reasoning) and explains the difference between 'could_not_verify' and 'unsupported'—essential for correct interpretation. The routing and tolerance behavior are also covered, making the description self-sufficient for an agent to invoke and use the tool correctly.

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% for both parameters, so baseline is 3. The description enriches the tolerance_pct parameter by explaining it overrides claim-implicated tolerance and recommending 1–2 for hallucination detection, which is not in the schema. The claim parameter is further illustrated with examples, adding practical guidance beyond the schema description.

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 the tool verifies natural-language factual claims against authoritative sources, with specific trigger phrases ('fact check', 'verify the claim that...'). It distinguishes between company-financial claims (via SEC EDGAR + XBRL) and other factual claims (grounded pipeline), and notes it replaces multiple sequential calls, making it distinct from general ask/research sibling 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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and describes the two routing paths for claim types. It does not name specific sibling tools as alternatives, but the 'Replaces 4–6 sequential calls' gives context on when this is preferred over a multi-step pipeline. Minor gap: no explicit 'do not use when...' or contrast with general ask tools.

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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