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uored

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), the description adds crucial behavioral context: what could_not_verify means (verification_error, not evidence), what unsupported means (no source found), and an explicit warning not to treat could_not_verify as evidence for/against the claim. This materially helps an agent interpret results correctly.

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 information-dense. It front-loads with user-intent phrasing, explains the routing logic, lists all verdict outcomes, and clarifies error semantics. Every sentence contributes; the structure is logical (start→routing→returns→caller caveats). Slightly verbose but justified for the complexity.

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?

Even without an output schema, the description enumerates all possible verdict values, mentions the actual value plus citation, and explicitly describes the two non-verdict cases (could_not_verify and unsupported). It also notes performance benefits (replaces 4–6 calls). Complete for a complex verification 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% with detailed descriptions for both parameters. The tool description adds value by explaining tolerance_pct overrides the implied tolerance and is capped at 5 by default, and by giving examples for the claim param. This goes beyond the schema baseline.

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 query patterns ('Is it true that…', 'fact check') and states the core function: natural-language claim verification against authoritative sources. It clearly distinguishes from siblings by noting it replaces 4–6 sequential calls and by splitting the handling of company-financial vs. other claims, which is a strong differentiation signal.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and provides a clear decision path (SEC EDGAR fast path for company-financial claims, grounded pipeline for anything else). It lacks an explicit 'do not use when…' exclusion, but the guidance is strong enough to be above average.

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

Several clusters overlap: the three random-activity tools are near-variants of the same action, ask_pipeworx_beta is currently identical to ask_pipeworx, and discover_tools/suggest_questions both serve capability discovery. The detailed descriptions help agents choose correctly, but the boundaries are not always crisp.

Naming Consistency3/5

All names are snake_case, but conventions are mixed: verb_noun (resolve_entity, discover_tools), bare verbs (remember, subscribe), adjective_noun (recent_alerts, deep_research), and noun compounds (polymarket_edge_tracker, entity_profile). Clusters are internally consistent, but there is no single pattern across the set.

Tool Count2/5

At 34 tools, this exceeds the 25+ threshold and feels bloated. The random-activity trio and the ask_pipeworx stable/beta/grounded trio add redundancy, and several meta-tools could be consolidated. The broad domain scope justifies some of the size, but not all of it.

Completeness3/5

The data query, memory, and Polymarket research surfaces are well covered, but the subscription lifecycle is incomplete: list_subscriptions invites cancellation yet there is no cancel/unsubscribe tool, creating a dead end. Other areas have minor workarounds but no other show-stopping gaps.

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