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Worms

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?

The description goes far beyond annotations by explaining the meaning of each verdict, especially the critical 'could_not_verify' vs 'unsupported' distinction. It discloses the internal routing (SEC EDGAR/XBRL fast path vs grounded pipeline) and the behavior of returning verbatim evidence with citations. Annotations already indicate read-only, idempotent, open-world, and non-destructive, and the description complements rather than contradicts them.

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 moderately long but every sentence carries meaningful information: trigger phrases, routing logic, verdict meanings, and a replacement note. It is front-loaded with the purpose and clearly structured, though it could be trimmed slightly by removing redundant trigger examples. Overall, it is efficient and well-organized.

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?

With no output schema and only two parameters, the description fully explains the return values and their semantics, including edge cases and error conditions. It also covers the tool's scope and limitations, making it complete for an agent to use correctly. The mention that it replaces multiple sequential calls adds operational context that rounds out the picture.

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 coverage is 100%, with both 'claim' and 'tolerance_pct' fully described in the schema. The description adds context about tolerance being capped at 5 and its use in grading, but this is marginal beyond the schema's own 'Overrides the tolerance implied by the claim wording' explanation. As such, it meets the baseline for high schema coverage without adding significant new parameter-level 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 states the tool's purpose as natural-language claim verification against authoritative sources, with explicit trigger phrases and examples. It distinguishes itself from sibling tools by focusing on fact-checking and by noting it replaces a multi-step pipeline, making its role unambiguous.

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 gives explicit guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It differentiates between company-financial claims and other factual claims, routing behavior, but does not explicitly contrast with specific sibling tools like ask_pipeworx_grounded. This is clear context but lacks formal exclusions.

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

Several tools have functionally overlapping purposes, most notably ask_pipeworx and ask_pipeworx_beta, which currently behave identically. The many prediction-market tools are each distinct but still leave boundaries that require careful reading, and the query/research tools (ask_pipeworx, deep_research, potentially validate_claim) have an ease of being confused. Fine verbal descriptions reduce but do not eliminate the ambiguity for an agent.

Naming Consistency3/5

All names are lowercase snake_case and many follow a verb_noun pattern, such as resolve_entity, get_classification, and list_subscriptions. However, the set is inconsistent overall: it mixes single verbs (remember, forget, subscribe), noun-style names (entity_profile, recent_alerts), and several prefixed families (pipeworx_*, polymarket_*). It's readable but not a coherent, uniformly applied convention throughout.

Tool Count2/5

34 tools in one server is well beyond the generally well-scoped 3–15 range. The server appears to bundle several unrelated domains together—WoRMS taxonomy, Pipeworx data research, Polymarket analysis, memory utilities, and subscriptions—creating avoidable cognitive load and making selection more difficult. Splitting into targeted servers would greatly improve the interface.

Completeness2/5

The server is named 'Worms' and includes a WoRMS taxonomy subdomain, but that subdomain only supports searching, classification, and common names—the rest of the marine taxonomy surface is missing (e.g., direct AphiaID record lookup, distributions, synonyms, hierarchical children). The remainder of the tools serve an entirely different data-research purpose, so the server is incomplete relative to its name AND the bundled extra domains add confusion rather than a coherent cohesive coverage.

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