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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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds significant behavioral context: explains was replaced, defines edge-case verdicts (could_not_verify vs unsupported), clarifies that could_not_verify is not evidence, mentions verification_error fields, and describes citation and reasoning. No contradictions with annotations.

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 a dense paragraph but every sentence contributes useful information: trigger phrases, routing, return values, caller warnings, and efficiency claim. It is longer than ideal but appropriately sized for the tool's complexity. The front-loaded trigger phrases aid recognition.

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 (two pipelines, six verdicts, error semantics) and the lack of an output schema, the description provides a comprehensive mental model. It covers the return structure, edge cases, and even caller guidance on how to interpret could_not_verify. This is complete for an agent to select and invoke the tool correctly.

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 detailed descriptions for both 'claim' and 'tolerance_pct', including examples and bounds. The tool description itself does not discuss parameters further, but the schema already carries the burden, so the baseline 3 applies. No additional meaning is added 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 opens with natural-language trigger phrases and explicitly states the tool's function: verification of factual claims against authoritative sources. It clearly differentiates from siblings like ask_pipeworx by focusing on verification with a defined verdict set and mentions the two distinct processing paths (SEC EDGAR structured 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 Guidelines4/5

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

The description provides explicit when-to-use guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It outlines the two routing paths and notes the tool replaces 4–6 sequential calls, implying efficiency. However, it doesn't explicitly name alternative tools or state when not to use it, so a small gap remains.

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

Many tools have overlapping purposes: ask_pipeworx_beta is a literal duplicate of ask_pipeworx, and ai_visibility_check/scans overlap with each other while the many prediction-market and edge tools cover similar ground. The verbose descriptions help somewhat, but an agent would frequently struggle to pick the right tool.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (get_package, list_versions), others noun_verb (ai_visibility_check, bet_research), and several are brand-specific (pipeworx_feedback, pipeworx_trending) with no uniform verb style. The mixed patterns make it hard to predict tool names.

Tool Count1/5

35 tools is far too many for a server named Packagist: only 4-5 tools relate to the PHP/Composer registry while the vast majority concern Pipeworx data lookups, Polymarket analysis, and memory utilities. The scope is severely mismatched with the server's name and apparent purpose.

Completeness2/5

For a Packagist registry server, the core read operations (search, get, list versions, stats) are present, but the server is cluttered with unrelated functionality and offers no package management actions. The tool set is not complete for any single, coherent domain, making it feel like two different servers merged into one.

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