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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

A4.5/5.0
Behavior5/5

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

The description adds substantial context beyond the annotations: it explains the two-tier routing (fast path vs grounded pipeline), return structure (verdict, citation, reasoning), and critically clarifies the semantics of 'could_not_verify' (check did not happen, with error field) versus 'unsupported' (no source coverage). This is exactly the kind of behavioral nuance needed for correct interpretation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-organized: it opens with trigger phrases and a definition, then outlines usage, processing paths, return values, and a highlighted IMPORTANT note. Every sentence adds value, and the structure flows logically from purpose to details to caveats. No filler or redundancy.

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 is complex (two processing paths, six possible verdicts, error semantics), yet the description covers all essential aspects: when to use, what routes, what it returns, and how to handle the critical could_not_verify vs unsupported distinction. Annotations cover safety, and no output schema exists, so the description carries the full burden—and it does so completely.

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%, so the schema already fully documents both parameters (claim and tolerance_pct) with examples and defaults. The description adds minimal parameter-specific information beyond what the schema provides—it references 'exact percent-delta math' but does not elaborate on tolerance usage. Therefore, the baseline of 3 is appropriate.

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 states a specific verb+resource: natural-language claim verification. It provides trigger phrases and explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also outlines the two distinct processing paths (SEC EDGAR/XBRL for company financials, grounded pipeline otherwise), which distinguishes it from generic search or QA tools.

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 gives clear usage context ('Use whenever the agent needs to check whether something a user said is factually correct') and explains how different claim types are routed. However, it does not name specific alternatives or state explicit exclusions (e.g., 'do not use for open-ended research'). This is strong but not fully explicit about when-not-to-use.

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 tools occupy adjacent territory—ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, and the Polymarket family has five overlapping analysis tools. The descriptions do a decent job of differentiating them, but an agent could still plausibly select the wrong variant in a mixed workflow.

Naming Consistency3/5

Most names follow a readable lowercase snake_case style, and there are coherent families like ask_pipeworx_*, polymarket_*, and pipeworx_*. However, conventions are mixed across the set—some are verb_noun (list_subscriptions, resolve_entity), some are bare verbs (forget, recall, reverse), and some are noun-phrase-only (entity_profile, recent_alerts)—so no single predictable pattern governs the whole server.

Tool Count2/5

33 tools is well past the 25+ threshold for a heavy tool surface, even accounting for the broad data-domain ambitions of the server. Many of these tools are meta-tools or thin variants of one another, so the set feels larger than necessary and imposes meaningful selection cost on an agent.

Completeness4/5

The server covers its apparent domain thoroughly: querying, grounded verification, deep research, entity resolution, profiles, comparisons, change tracking, claim validation, memory, subscriptions, and prediction-market analytics are all represented. Minor gaps exist—such as no direct tool for retrieving a raw pipeworx:// citation record and no account/auth flow—but agents can generally complete core workflows without dead ends.

Resources