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Bls

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.

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing the internal routing (SEC EDGAR/XBRL fast path vs. grounded pipeline), the return verdicts, and critically the distinction between 'could_not_verify' (check did not happen) and 'unsupported' (no source found). This prevents misinterpreting a failed check as evidence, which is essential behavioral context.

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 dense but well-organized, leading with trigger phrases, then use case, then pipeline details, then return values and caveats. Every sentence carries meaning, though the long sentences with viele dashes might be slightly harder to parse. Still, it earns high marks for efficiency.

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 there is no output schema, the description thoroughly explains the return values (verdicts, actual value, reasoning) and clarifies ambiguous outcomes. It covers the major execution paths and the important 'could_not_verify' pitfall. This is complete for a tool of this complexity.

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?

The input schema already fully documents both parameters (claim and tolerance_pct) with clear descriptions and defaults. The tool description adds no additional parameter-level information, so the baseline of 3 applies. It does mention 'exact percent-delta math' but that is about internal computation, not parameter semantics.

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 identifies the tool as natural-language claim verification, with specific trigger phrases like 'fact check' and 'verify the claim'. It distinguishes itself from sibling tools by focusing on factual claims against authoritative sources, and even breaks down the two processing paths (SEC EDGAR fast path vs. grounded pipeline).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the automatic routing for financial vs. other claims. This gives clear when-to-use context, though it doesn't mention specific exclusions or alternatives, the scope is comprehensive and unambiguous.

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
Disambiguation2/5

The ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded trio are nearly indistinguishable, with beta currently behaving identically to the stable version. Entity_profile, recent_changes, and compare_entities also overlap heavily as multi-source company research tools, and the six polymarket tools create additional boundary confusion.

Naming Consistency2/5

Most tools use snake_case, but there is no consistent verb_noun pattern: some are imperative phrases (ask_pipeworx, bls_get_series, resolve_entity), while others are noun phrases (entity_profile, pipeworx_feedback, polymarket_edges, recent_alerts). Even within the bls_* family, bls_latest breaks the verb pattern established by bls_get_series and bls_search.

Tool Count2/5

35 tools is well above the 25+ threshold for 'too many', and the server is named Bls yet only four tools actually serve BLS data. Most of the remaining tools cover unrelated domains like Polymarket arbitrage, memory, feedback, and general Pipeworx routing, making the count feel inflated for the apparent purpose.

Completeness3/5

The four BLS-specific tools cover search, browse, historical series fetch, and latest value, but there is no multi-series fetch or series metadata detail, which is a notable gap for a BLS-focused server. The broader Pipeworx toolset is extensive, but the lack of a coherent stated domain makes completeness hard to evaluate as a unified surface.

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