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uooks

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max 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.",
+  "type": "number"
+}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the bar is lower. The description adds crucial nuances: the special meaning of could_not_verify (not evidence), unsupported (no source coverage), and the exact verdict set. This goes beyond the annotations and clarifies caller expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Although the description is long, every sentence earns its place: example triggers, usage rule, routing logic, return values, and special verdict semantics. It is front-loaded with purpose and structured logically from usage to outcomes. No fluff or redundancy.

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, the description fully explains return values (verdict options, citation, reasoning) and the meaning of exceptional verdicts. It also covers fallback behavior and error context (verification_error). For a tool with 2 params and 1 required param, this is complete and self-contained.

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 the description doesn't need to explain parameters. It adds extra guidance on tolerance_pct, including the suggestion to set 1–2 for hallucination detection and the default cap of 5, which is not in the schema. This provides actionable meaning beyond the structured definition.

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 example phrasings ('Is it true that…' / 'fact check') and a clear verb+resource: natural-language claim verification. It distinguishes itself from siblings by describing a consolidated pipeline that replaces 4–6 sequential calls, and by specifying the domain (company-financial vs. any other factual claim).

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 explicitly states when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and gives guidance on routing (SEC EDGAR fast path vs. grounded pipeline). It does not explicitly name alternative tools or when not to use, so it stops 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

A4.1/5.0
Disambiguation3/5

Several tool families overlap at the boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same 5,724 tools, and ask_pipeworx_beta is currently functionally identical to ask_pipeworx. The Polymarket family is large but each member has a fairly distinct role (research vs. edge scan vs. fill risk vs. tracking); the memory trio and book tools are clear.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (get_book, create, search_books, resolve_entity, list_subscriptions), but there are notable exceptions: recall/remember/forget are bare verbs without a domain prefix, ask_pipeworx begins with a verb but doesn't follow the noun-object structure, and ai_visibility_check/generate_llms_txt break the pattern. It's readable and mostly predictable, but not uniform.

Tool Count3/5

35 tools is heavy and exceeds the typical well-scoped range, but the server is a meta-platform exposing a universal data router plus prediction-market analysis, book lookup, memory, subscriptions, and several composite research tools. Each tool appears to earn its place, though the set feels sprawling and would benefit from consolidation of the ask_pipeworx variants.

Completeness4/5

Coverage is thorough within the apparent domains: data lookup has multiple tiers (casual, grounded, deep research, claim validation), the Polymarket workflow is complete from research to edge discovery to fill-risk verification, memory has save/retrieve/delete, and subscriptions have create/list/cancel/pull. Minor gaps exist (e.g., book author search by name only via Open Library key, no direct tool for invoking a specific raw data pack), but nothing that would strand an agent.

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