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Datos Cl

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?

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description reveals critical behavioral traits: the semantic difference between could_not_verify (check did not happen) and unsupported (no source found), the internal routing to SEC EDGAR versus the grounded pipeline, and the use of 'verbatim evidence' and 'exact percent-delta math.' It also warns callers not to treat could_not_verify as evidence, which is essential for correct output 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 appropriately sized for the tool's complexity, using examples, a clear routing explanation, a return-verdict list, and an important error-semantics callout. Every sentence adds value, and the structure is front-loaded with the core purpose and trigger phrases, making it easy to scan.

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 (routing, multiple verdicts, error semantics) and the absence of an output schema, the description is remarkably complete. It covers inputs, outputs, error cases, source routing, and efficiency benefits, leaving no critical ambiguity for an agent deciding whether and how to invoke it.

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 description coverage is 100% for both parameters (claim and tolerance_pct), so the baseline is 3. The description itself does not add significant parameter-level detail beyond what the schema already provides, though it does mention tolerance indirectly through the approximately_correct verdict. No contradiction or gap requiring extra compensation.

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 trigger phrases and states 'natural-language claim verification against authoritative sources,' naming the specific verb ('verify') and resource ('claims'). It distinguishes itself from sibling tools by emphasizing fact-checking with verdicts and the structured SEC EDGAR fast path for financial claims, clearly differentiating from general grounded Q&A tools like ask_pipeworx_grounded.

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 directs use 'whenever the agent needs to check whether something a user said is factually correct' and explains the routing between company-financial claims and other factual claims. It also notes the tool replaces 4–6 sequential calls, implying efficiency. However, it does not name specific alternative tools or provide explicit 'when not to use' exclusions, though the context is otherwise clear.

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

A3.6/5.0
Disambiguation4/5

Tools are generally distinct in purpose, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, and deep_research. Descriptions clearly differentiate them, but an agent might still need to carefully choose.

Naming Consistency4/5

Most tool names follow a consistent snake_case pattern with clear prefixes (e.g., pipeworx_*, polymarket_*). A few names like compare_entities and validate_claim break the pattern, but overall it is predictable.

Tool Count2/5

With 40 tools, the server is overly large for a single coherent set. Many tools are meta or auxiliary, but the count exceeds the 'too many' threshold, making it hard for agents to navigate.

Completeness4/5

The tool set covers data retrieval, Chilean open data, Polymarket, memory, and monitoring comprehensively. Minor gaps exist (e.g., no direct trading for Polymarket), but the surface is complete for the stated purpose.

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