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Glama

Census Trade

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. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max 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.",
+  "type": "number"
+}
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses critical behavioral nuances: 'could_not_verify' means the check did not happen and must not be treated as evidence, carrying a verification_error object; 'unsupported' means no source coverage. It also clarifies the verdict types and the use of verbatim evidence with pipeworx:// citations. These details directly address potential misuse and are not present in the annotations or schema.

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 front-loaded with trigger phrases and examples, making it immediately actionable. It is somewhat lengthy, but most sentences add value—covering routing, output contract, and error handling. However, it unnecessarily repeats tolerance details already present in the schema, which is a minor redundancy. Overall, it is well-structured for an AI agent, though not perfectly concise.

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 and the absence of an output schema, the description provides a complete picture: it specifies the exact return fields (verdict, actual value with citation, reasoning), explains the distinction between 'could_not_verify' and 'unsupported', and describes the routing logic. This fully covers what an agent needs to invoke the tool and interpret results correctly, with no significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for both parameters, and the schema already contains detailed descriptions for 'tolerance_pct' including its range, override semantics, and default cap. The tool description largely repeats this information (e.g., 'Overrides the tolerance implied by the claim wording' and 'Default: implied by wording, capped at 5') without adding new meaning. The 'claim' parameter is also adequately described in the schema with examples, so the description adds no significant extra parameter semantics.

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 explicitly states the tool performs natural-language claim verification against authoritative sources, with a clear verb-resource pair ('validate claim'). It distinguishes itself from sibling tools by describing a unified pipeline that handles both financial and general claims, and by specifying exact output verdicts. This makes the purpose unmistakable and unique among the sibling list.

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 provides explicit usage guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It further differentiates use cases by routing company-financial claims through SEC EDGAR/XBRL fast path and all other claims through the grounded pipeline. It also notes that the tool replaces 4–6 sequential calls, giving clear context on when to invoke it over multi-step alternatives.

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

There is substantial overlap among the question-routing tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, and validate_claim all funnel natural-language queries into similar source lookups. ai_visibility_check and scan_competitor_ai_presence also cover nearly the same capability, making tool selection genuinely ambiguous.

Naming Consistency3/5

All names are snake_case and many follow a verb_noun pattern (census_exports, list_subscriptions, validate_claim), but the convention drifts with noun-first names like entity_profile, pipeworx_trending, and recent_alerts, and bare verbs like remember, recall, forget, and subscribe. It is readable but not a tight, predictable pattern.

Tool Count2/5

35 tools is too many for a server named 'Census Trade' when only 4 of them actually relate to Census trade data. The remaining 31 form an unrelated general-purpose data, prediction-market, memory, and subscription toolkit, making the surface feel bloated and badly scoped relative to the server's apparent purpose.

Completeness3/5

The Census trade core covers exports, imports, trade balance, and monthly trends, which handles the central queries, but there is no HS-code catalog, country metadata, or state/port-level breakdown, leaving notable gaps for a trade-data domain. If judged as the broad Pipeworx gateway it appears to actually be, coverage is richer, but that contradicts the server name and weakens overall coherence.

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