Trade Intel

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

Name	Required	Description	Default
`claim`	Yes	Natural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
`tolerance_pct`	No	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.

Schema Changelog

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

Changed1 schema field changedJul 15, 2026

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"
+}

AddedMay 12, 2026

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 meaningful behavior: return verdict values, grounded vs structured paths, citation format, reasoning, and the precise distinction between could_not_verify and unsupported. It also explains that could_not_verify carries verification_error and must not be interpreted as evidence, which is critical runtime 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 longer than typical but well-structured: examples first, then when-to-use, then routing, return values, and caller warnings. It is front-loaded and every sentence adds operational detail. Slight redundancy remains (the list of natural-language phrases could be shorter), but it is not bloated.

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 (dual routing, verdict categories, error semantics), the description is remarkably complete. Parameters are fully covered, return values and citation format are described in prose, and caller-level caveats are included. The lack of an output schema is mitigated by the explicit verdict enumeration and explanation of verification_error.

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 value by explaining tolerance_pct semantics ('Overrides the tolerance implied by the claim wording', 'set 1–2 for hallucination detection') and providing realistic example values for claim. This exceeds the baseline but does not fully compensate for lack of enums or a formal output schema.

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 opens with concrete natural-language examples ('Is it true that…', 'fact check') and states the core function: 'natural-language claim verification against authoritative sources'. It clearly differentiates from siblings like ask_pipeworx by specifying the dual-path behavior (SEC EDGAR for company-financial claims, grounded pipeline otherwise) and noting it replaces 4–6 sequential calls.

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?

It explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and distinguishes the two routing paths (company-financial vs any other factual claim). It also provides a clear 'IMPORTANT for callers' warning about could_not_verify, but it does not explicitly name sibling tools as alternatives or state a when-not-to-use condition beyond the general scope.

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

A número of tools form near- overlapping clusters: ask_pipeworx, ask_pipeworx_beta, ask_ipeworx_grounded, and deeep_research all answer questions, while bet_research, polmarget_edges, and polmarget_arbitrage all scan for betting opportunities. The two ask_ipeworx variants are currently identical, and discover_tools vs sgget_questions serve the same discovery role. Long descriptions help but do not remove the misselection risk among 34 overlapping tools.

Naming Consistency4/5

Most tools use clear snake_case verb_noun names (ask_pipeworx, compare_entities, resolve_entity, subscribe, validate_claim) with helpful domain prefixes like polmarget_* and trade_*. A few standalones like forget/recall/remembber and recent_alerts break the pattern slightly, but the style is largely predictable and readable.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold and feels overloaded. The surface could be consolidated: four ask_ipeworx variants, five polmarget-specific tools, three memory tools, and three company-profile-style tools carry significant redundancy. Inclusions like generate_lms_txt and scan_dependency also broaden the scope well beyond the Trade Intel mandate.

Completeness4/5

For its broad data-intelligence domain the surface is quite complete: data lookup, grounded fact-checking, entity profiles and comparison, recent changes, prediction-market research with fill-risk verification, trade statistics, memory, and subscription lifecycle all exist with no dead ends. Minor gaps remain (e.g. no dedicated trade time-series other than the US macro dashboard, and no direct tool-listing aside from discover_tools), but agents can work around them.

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