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Glama

Comtrade

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?

Even though annotations already mark the tool read-only and non-destructive, the description adds vital behavioral context: the distinction between company-financial and other claims, the meaning of special verdicts (could_not_verify and unsupported), and a warning that could_not_verify should not be treated as evidence. This is critical to correct invocation and result interpretation, going beyond what annotations provide.

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 dense but purposeful: it opens with concrete trigger phrases, explains routing, lists return values, and warns about a common misuse. Each sentence adds value, but the length and detail are substantial, making it slightly less scannable than an ideal concise description. Still, given the tool's complexity, the length is justified.

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?

The tool has multiple routing modes, six verdicts, and important error semantics. The description covers all these, including the return format (verdict, value with citation, reasoning) and the efficiency benefit (replaces 4–6 calls). No output schema exists, so the description must and does explain what callers will receive, making it complete for correct invocation and result interpretation.

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?

Both parameters are documented in the schema (100% coverage), so the baseline is 3. The description goes further by explaining tolerance_pct in detail—e.g., how to override the default for hallucination detection and that the default is capped at 5—and providing natural-language examples for the claim parameter. This added context 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 identifies the tool as natural-language claim verification against authoritative sources, with a specific verb ('validate') and resource ('claim'). It distinguishes itself from sibling tools like ask_pipeworx by focusing on fact-checking claims, and gives example phrasings ('Is it true that…', 'fact check') that pinpoint its exact use.

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?

The description states when to use the tool: 'whenever the agent needs to check whether something a user said is factually correct.' It also provides internal routing context (company-financial vs. other claims), but does not explicitly name alternatives or when NOT to use this tool, which would fully clarify its place among siblings.

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
Disambiguation3/5

Some tools have near-identical purposes (ask_pipeworx vs ask_pipeworx_beta are currently identical; polymarket_arbitrage vs polymarket_edges both find opportunities), but detailed descriptions and distinct input patterns mostly help an agent choose. A few discovery/verification tools (suggest_questions vs discover_tools, validate_claim vs ask_pipeworx_grounded) also overlap, creating residual ambiguity.

Naming Consistency3/5

All names are snake_case and many use domain prefixes (comtrade_, polymarket_, pipeworx_), but the set mixes verb_noun (compare_entities, discover_tools), bare verbs (remember, forget, subscribe), and noun phrases (entity_profile, recent_changes). This inconsistency, plus the use of domain-specific prefixes as a substitute for a uniform verb_noun style, makes the naming pattern only moderately predictable.

Tool Count2/5

35 tools is well beyond the typical well-scoped range, and the vast majority (prediction markets, memory, subscriptions, feedback, npm dependency checks) have nothing to do with the server's apparent Comtrade trade-data purpose. This bloat makes the set feel unfocused, even though some meta-tools serve a general data-access mission.

Completeness4/5

For the Comtrade trade-data domain, the four comtrade_* tools cover country codes, top commodities, top partners, and detailed bilateral trade values — enough for most queries, with minor gaps like time-series trends or tariff lookups. For the broader Pipeworx data-access scope, the set is extensive (ask_pipeworx, deep_research, entity_profile, validate_claim, subscriptions), so no severe dead ends are apparent.

Resources