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Color

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 only indicate that the tool is read-only, idempotent, and non-destructive. The description goes far beyond by explaining the dual pipeline, the meaning of each verdict, the crucial distinction between 'could_not_verify' (check did not happen) and 'unsupported' (no source), and the tolerance override behavior. This adds critical context for callers.

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 longer than average, but it is front-loaded with trigger phrases and every sentence provides necessary information about usage, behavior, return values, and edge cases. It is appropriately detailed for a tool with this complexity, though slightly verbose.

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?

Even without an output schema, the description fully specifies what the tool returns (verdict, value with citation, reasoning), explains error cases (could_not_verify with verification_error), and notes the efficiency benefit of replacing multiple sequential calls. This makes the tool self-contained and clear for an agent.

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 provides detailed descriptions for both parameters (claim and tolerance_pct) with examples and defaults. The tool description adds some context (e.g., 'exact percent-delta math') but does not significantly improve parameter understanding beyond what the schema already offers.

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 explicitly identifies the tool as claim verification against authoritative sources, with a clear verb ('verify') and target resource ('claims'), and lists natural-language trigger phrases. It distinguishes itself from sibling tools by focusing on factual claim checking rather than general search or research.

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 states clearly when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims (fast path) and other claims (grounded pipeline). However, it does not explicitly name alternative tools or state when not to use, so it falls short of a 5.

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

A3.9/5.0
Disambiguation2/5

Several tools are near-identical: ask_pipeworx and ask_pipeworx_beta are explicitly the same right now, and the three ask_pipeworx variants plus deep_research all route the same underlying catalog. The six polymarket_* tools also have heavily overlapping purposes, requiring deep reading to choose correctly. Most other tools are distinguishable, but these clusters create real misselection risk.

Naming Consistency3/5

The set is uniformly snake_case and mostly descriptive, with consistent micro-families like remember/recall/forget and polymarket_*. However, there is no single convention across the server: verb_noun names (ask_pipeworx, list_subscriptions) mix with noun-first names (entity_profile, bet_research, nearest_color), and the Pipeworx brand is used as both prefix and suffix. Readable but inconsistent.

Tool Count2/5

At 34 tools, the surface is far larger than a typical well-scoped server, and the count is especially unjustified for a server named 'Color' where only three tools relate to that name. The breadth stems from bolting a full data-research platform, prediction-market suite, memory store, and subscription system onto what appears to be a simple utility. Too heavy.

Completeness4/5

For the dominant Pipeworx research domain, the set is unusually thorough: query, grounded verification, deep research, entity resolution, comparisons, claim validation, change feeds, subscriptions, alerts, memory, and discovery are all present. Minor gaps exist (e.g., no direct account management tool, and color coverage only includes convert/contrast/nearest with no palette generation), but these are workaround-able. Completeness is strong for the real domain, if mismatched with the server name.

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