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Transport Feeds

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?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds significant behavioral context: it explains the two pipeline paths, the exact verdict categories, the pipeworx:// citation format, and crucially distinguishes 'could_not_verify' (system failure) from 'unsupported' (no source found). This provides important error-handling semantics beyond what annotations convey.

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 relatively long but well-structured: trigger phrases, core purpose, routing details, return summary, important caller notes, and efficiency claim. Every section earns its place for a complex verification tool. It could be tightened slightly, but it remains readable and front-loaded with the most critical usage guidance.

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?

Despite the absence of an output schema, the description adequately covers the verdict list, error semantics, routing behavior, and parameter nuances. For a tool with only two simple parameters and no nested objects, this level of explanation is comprehensive and leaves little ambiguity about how to invoke the tool correctly.

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

Parameters4/5

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

Schema coverage is 100%, so both parameters are already documented. The description adds meaning by explaining that tolerance_pct overrides the tolerance implied by claim wording and giving a concrete use case (set 1–2 for hallucination detection). Claim examples clarify the expected natural-language format. This goes beyond the schema's basic descriptions.

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 concrete trigger phrases ('Is it true that…', 'fact check') and then states the core function: natural-language claim verification against authoritative sources. It clearly distinguishes itself from sibling tools like ask_pipeworx by framing this as a verification tool, not just an information retrieval tool, and notes it replaces 4–6 sequential calls.

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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and details two distinct routing paths (SEC EDGAR for company-financial claims, grounded pipeline for everything else). It doesn't name alternative sibling tools or provide when-not-to-use conditions, but the 'Replaces 4–6 sequential calls' provides strong practical guidance on when this is the right choice.

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

A3.7/5.0
Disambiguation2/5

There is substantial overlap among the many question-answering tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, compare_entities, entity_profile, recent_changes) and the prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). While each has nuanced differences, agents will struggle to select the right one, especially with several 'ask_pipeworx' variants that behave nearly identically.

Naming Consistency3/5

Most tool names use snake_case, but patterns vary widely: some are verb_noun (list_feeds, read_feed, subscribe, unsubscribe, remember), others are noun_phrases (polymarket_arbitrage, pipeworx_trending, entity_profile), and a few like 'ask_pipeworx' and 'bet_research' don't follow a consistent structure. The mixed conventions make prediction of new tool names difficult.

Tool Count2/5

With 34 tools, this is far too many for a server named 'Transport Feeds'. The majority of tools are unrelated to transport feeds, covering generic data research, prediction markets, and memory utilities. The count overwhelms any focused purpose and would require extensive discovery to navigate.

Completeness3/5

For the actual data-research and prediction-market functions, the surface is quite complete—covering lookups, comparisons, grounded verification, arbitrage scans, fill risk, trending, and subscriptions. However, for the declared domain (transport feeds), there are only two feed-specific tools (list_feeds, read_feed) with no write/update/delete operations, leaving obvious gaps.

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