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Glama

Vies Eu

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations provide readOnly/openWorld/idempotent/non-destructive hints, but the description adds extensive behavioral context: the full verdict set (confirmed, approximately_correct, refuted, inconclusive, unsupported, could_not_verify), the distinction between could_not_verify (check did not happen, carries verification_error) and unsupported (no source found), and the pipeline behavior with verbatim evidence and citation. This far exceeds the structured fields and gives the agent critical interpretation rules.

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 average but systematically structured: trigger phrases, usage rule, routing logic, return contract, caller warnings, and efficiency rationale. It front-loads the most recognizable user intents and keeps warnings together at the end. Some phrasing is slightly repetitive, but the complexity of the tool justifies the length.

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?

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: verdict list, grounded/structured actual value with citation, reasoning, and error semantics for verification_error. It also explains the two pipeline paths and the meaning of 'unsupported.' This is complete enough for an agent to invoke the tool and interpret results 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?

The input schema already documents both parameters with 100% coverage, but the description adds important semantic nuance: tolerance_pct overrides the tolerance implied by the claim wording, suggests values 1–2 for hallucination detection, and notes the default cap at 5. It also gives concrete example claim formats for the 'claim' parameter, supplementing the schema without redundancy.

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 common user phrasings ('Is it true that…', 'fact check', 'verify the claim that…') and then states the core verb: natural-language claim verification against authoritative sources. It clearly identifies the tool's resource (factual claims) and scope (company-financial via SEC EDGAR + XBRL fast path; all other claims via grounded pipeline), distinguishing it from general-purpose research or ask tools. While it does not explicitly name sibling tools, the domain and behavior are specific enough to differentiate it.

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,' giving a clear trigger condition. It also explains the routing behavior for company-financial vs. other claims and notes that it replaces 4–6 sequential calls, which informs when to choose this tool over a manual multi-step pipeline. It does not name sibling tools or state explicit exclusions, but the guidance is strong.

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

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical to the stable router right now), ask_pipeworx_grounded, and deep_research all route the same class of questions, making mis-selection easy. The six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also blur together around edge detection and arbitrage, further muddying tool boundaries.

Naming Consistency4/5

All 33 tools use consistent lowercase snake_case naming, and most follow a clear verb_noun pattern (check_vat, compare_entities, resolve_entity, unsubscribe). A handful of noun-style names (entity_profile, bet_research, polymarket_edges, recent_alerts) deviate from the verb-first pattern but are still predictable and readable.

Tool Count2/5

33 tools is beyond the 25+ threshold for a coherent set, and the scope is sprawling: universal data routing, prediction-market analytics, VAT validation, AI visibility, memory, subscriptions, npm dependency scanning, and feedback. While each sub-domain has reason to exist, bundling them all into one server creates a kitchen-sink feel with too many entry points.

Completeness3/5

The core data-routing domain is well covered: universal router, grounded mode, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and search-within. VAT has check + status, and memory/subscription lifecycles are complete. However, there is no standalone tool to fetch a raw pipeworx:// record that citations reference, and the extreme breadth means no single domain is exhaustively fleshed out.

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