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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.

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond annotations by explaining the meaning of each verdict type, especially the critical distinction between 'could_not_verify' (check did not happen, carries error details) and 'unsupported' (we looked, no source). It also discloses the two processing paths and that it replaces 4–6 sequential calls. Annotations are not contradicted and are enriched by this 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 long but every sentence carries weight—no fluff. It front-loads with concrete query examples and then covers behavior and caller warnings. A slight deduction for density; the text could be broken into shorter chunks for easier parsing, but it is still well-structured.

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?

For a tool with two parameters, no output schema, and no nested objects, this description is exceptional. It fully explains the return values (verdict, actual value, citation, reasoning), error semantics, and routing behavior. An agent has everything needed to invoke and interpret results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds meaningful semantics: it defines what 'tolerance_pct' does, explains how it overrides claim wording and provides recommended values for hallucination detection (1–2). It also clarifies the default behavior (capped at 5). This goes beyond the schema's bare definitions.

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 performs 'natural-language claim verification against authoritative sources' with a specific verb (verify) and resource (claims). It differentiates from siblings by providing example invocation phrases and explicitly covering both company-financial claims and any other factual claim.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' giving clear context. It also describes the internal routing logic (SEC EDGAR fast path vs. grounded pipeline), but it does not name sibling alternatives or state outright when not to use this tool, so it misses full when-not/alternative guidance.

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

Multiple tools have overlapping purposes, notably ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded which are near-identical in function. The polymarket_* family also has several members with closely related scopes, and the large number of data-query tools makes it hard to choose the right one without careful reading.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: some are verb-first (list_subscriptions, validate_claim), others are noun-first (entity_profile, bet_research), and proper-noun prefixes like pipeworx_ and polymarket_ are used liberally. The gitlab_* tools follow a clear verb_noun pattern, but the rest of the set is mixed.

Tool Count2/5

With 36 tools, the server is overloaded, especially given that only 5 are GitLab-related while the rest are a sprawling data-access toolkit. Many tools could be consolidated (e.g., the ask_pipeworx variants), and the count exceeds what is reasonable for a focused GitLab server.

Completeness2/5

For a server named Gitlab, the coverage is severely incomplete: only list/get operations exist for projects, issues, and MRs, with no create, update, or delete capabilities. The broader data tools are more complete, but the nominal purpose of the server is clearly not fulfilled.

Resources