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Glama

Blockchair

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

A4.4/5.0
Behavior5/5

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

Beyond the read-only, open-world, idempotent annotations, the description adds rich behavioral detail: the verdict enum, the meaning of 'could_not_verify' (failure, not evidence), the difference between 'unsupported' and 'could_not_verify', the fallback pipeline, and the citation format. This significantly exceeds what the annotations provide and is not contradictory.

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 long but thoroughly organized, front-loading natural-language triggers and then covering purpose, routing, verdicts, and important caller warnings. The 'IMPORTANT for callers' section and explicit verdict list justify the length, though the repeated trigger phrases could be tightened. It is concise for the amount of critical detail conveyed.

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?

Since there is no output schema, the description fully explains return values, verdict semantics, error handling (verification_error), and unsupported cases. It also explains the tool's composite nature (replacing 4-6 calls) and covers routing decisions. This makes it complete for a complex tool with no structured output definition.

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%, with both 'claim' and 'tolerance_pct' already documented in the input schema. The description does not add parameter-specific nuance beyond the schema; it only references tolerance indirectly via 'exact percent-delta math'. Thus the baseline of 3 applies.

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 explicitly identifies the tool as a natural-language claim verifier with trigger phrases like 'fact check' and 'verify the claim that…'. It also lists the specific verdicts returned and distinguishes itself from generic Q&A tools by framing it as a replacement for a multi-step pipeline, and by splitting claims into financial vs. other categories. This is specific and differentiates it from siblings.

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?

It clearly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides routing context for financial vs. non-financial claims. However, it does not explicitly name alternative tools or state when not to use it, so a 4 rather than 5.

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

B3.3/5.0
Disambiguation2/5

The major clusters are distinct (blockchain explorer, memory, subscriptions), but several tools have unclear boundaries: ask_pipeworx_beta is currently identical to ask_pipeworx, the three ask_pipeworx variants and deep_research all route questions, and the five Polymarket tools overlap heavily on edge detection. An agent would struggle to pick the right query tool or prediction-market tool without reading very long descriptions.

Naming Consistency2/5

Naming is a mix of single-word nouns (address, block, node, transaction, stats), verb-noun snake_case (validate_claim, generate_llms_txt), and noun-phrase snake_case (entity_profile, bet_research), with no consistent style or verb convention. There is no predictable pattern an agent can generalize from.

Tool Count2/5

36 tools is well above the comfortable range, and most are meta-tools for Pipeworx, Polymarket, memory, and subscriptions rather than Blockchair blockchain functionality. A large share of the count is redundant query and edge-analysis variants, so the size adds confusion rather than capability.

Completeness4/5

As a read-only research and monitoring gateway, the surface is fairly complete: universal routing, grounded answers, entity resolution, profiles, comparisons, claim validation, memory, and subscriptions all have lifecycle coverage. Relative to the Blockchair name, the blockchain side is thin but covers address, block, transaction, node, and stats, with only minor gaps like mempool or raw script details that agents can work around.

Resources