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Statec Lu

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?

With annotations already declaring read-only, open-world, and idempotent behavior, the description adds significant context beyond annotations: it explains the routing logic, the verdict list, and critically distinguishes 'could_not_verify' (check didn't happen) from 'unsupported' (no source exists). It also warns callers not to treat 'could_not_verify' as evidence, which is a key behavioral nuance.

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 lengthy but information-dense, with trigger phrases front-loaded and structured progression from behavior to return value to caveats. Every sentence contributes, though some could be tightened; the efficiency of the '4–6 sequential calls' note adds value without excessive verbosity.

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 cases) and absence of an output schema, the description is remarkably complete. It explains what is returned (verdict, value with citation, reasoning), clarifies the distinction between 'could_not_verify' and 'unsupported', and even notes the performance benefit. Minor omissions like confidence scores do not hinder usability.

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 baseline is 3. The description enriches parameter understanding by explaining how tolerance_pct overrides the claim's implied tolerance and suggesting set 1–2 for hallucination detection. It also ties the claim format to the SEC EDGAR math, adding value beyond the 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 clearly states the tool's purpose: natural-language claim verification with specific trigger phrases like 'fact check' and 'verify the claim'. It distinguishes itself by describing the SEC EDGAR fast path and fallback grounded pipeline, and explicitly notes it replaces 4–6 sequential calls, setting it apart from potential alternatives.

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 provides clear guidance on when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates claim types (company-financial vs. other) but does not explicitly state when not to use it or name alternative tools, leaving some room for ambiguity.

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

A3.9/5.0
Disambiguation3/5

The three ask_pipeworx variants (stable, beta, grounded) plus deep_research and validate_claim create real selection ambiguity — an agent could easily pick the wrong one. Many other tools (entity_profile, bet_research, scan_dependency) are clearly distinct, but the overlapping meta-query tools muddy the boundary.

Naming Consistency3/5

Naming is a mix of verb-initial (get_data, resolve_entity, generate_llms_txt, scan_dependency) and noun-initial (dataflow_structure, entity_profile, polymarket_edges, pipeworx_trending) conventions. The ask_pipeworx family and Polymarket cluster are internally consistent, but there is no single predictable pattern across the set.

Tool Count2/5

34 tools is heavy, and the server named 'Statec Lu' (Luxembourg statistics) carries 30+ tools for prediction markets, npm dependencies, AI visibility, memory, and subscriptions. It reads as an everything-server rather than a focused statistics integration; most tools have nothing to do with STATEC.

Completeness4/5

Within the STATEC domain, list_dataflows → dataflow_structure → get_data is a complete browse-and-query workflow. The broader domains also have good coverage (memory save/recall/forget, subscription list/create/cancel, rich Polymarket research tools). Minor gaps like no data-format conversion or direct 'latest value' shortcut exist, but they are workable.

Resources