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Edamam

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 read-only, open-world, and idempotent hints, but the description adds crucial behavioral context: the distinction between could_not_verify (check did not happen) and unsupported (no source exists), and the warning that could_not_verify is not evidence. It also discloses the internal routing logic (SEC EDGAR vs grounded pipeline) and return structure.

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 information-dense and well-structured, starting with example phrasings, then purpose, routing, return value, and important warnings. Every sentence adds value, though the 'Replaces 4–6 sequential calls' is a nice-to-have and the text is fairly long, keeping it from a perfect 5.

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 and absence of an output schema, the description thoroughly covers use cases, internal routing, verdict types, citation format, reasoning, and edge-case semantics. It also warns about the could_not_verify vs unsupported distinction, which is essential for correct handling.

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?

The input schema already contains detailed descriptions for both parameters (100% coverage), so the baseline is 3. The description adds marginal value by noting 'exact percent-delta math' and the tolerance override for hallucination detection, but does not deeply elaborate on parameter formatting or constraints 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 clearly states the tool performs natural-language claim verification against authoritative sources, with a specific verb (verify) and resource (claims). It distinguishes itself from sibling tools by focusing on factual correctness judgments with a verdict output and explicitly mentions replacing multi-step pipelines.

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 provides explicit guidance on when to use ('Use whenever the agent needs to check whether something a user said is factually correct') and explains the two routing paths for financial vs other claims. However, it does not explicitly name alternative tools or state exclusions, though it implies a consolidated workflow.

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

The tool set includes multiple overlapping tools (ask_pipeworx variants, many polymarket tools) that serve similar purposes, and there is a sharp domain split between food tools and Pipeworx data tools, making it hard for an agent to choose correctly.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun (search_food), noun_verb (nutrition_analysis), prefixed (pipeworx_feedback, polymarket_arbitrage), and no pattern. Some use underscores, some use whole words.

Tool Count3/5

34 tools is on the high side, and the scope is extremely broad (food, data queries, prediction markets, subscriptions), which could overwhelm an agent, but the number alone is not extreme.

Completeness2/5

As a food API server, it is missing key features like recipe details, ingredient substitution, or meal planning, while including many extraneous tools. The data analytics tools are extensive but not aligned with the server's stated purpose.

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