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Htmltext

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. First observed

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/non-destructive, and the description adds substantial non-obvious behavior: the dual pipeline (SEC EDGAR vs. grounded), the special semantic of 'could_not_verify' being a failure to check (not evidence), and the distinction from 'unsupported'. This is exactly the contextual depth needed beyond structured fields.

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 long but information-dense. Every sentence earns its place: example queries, routing rules, return enumeration, critical caveats, and performance benefits. It is front-loaded with the most important purpose statements, and the caveats about could_not_verify are crucial. Slightly verbose but proportional to complexity.

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 carries the burden of explaining return values, and it delivers: enumerates all verdict types, mentions citation and reasoning, distinguishes error cases, and explains the two underlying pipelines. It even preempts common misuse with the 'could_not_verify' warning. The tool is complex and this description is remarkably complete.

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% with good parameter descriptions, so baseline is 3. The description adds value by explaining the tolerance_pct override behavior (default from wording, capped at 5, and guidance to set 1-2 for hallucination detection) and by giving examples that flesh out claim format. This goes beyond the schema's explicit field 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 natural-language query patterns ('Is it true that…', 'fact check', 'verify the claim that…'), clearly identifying the tool as claim verification against authoritative sources. It distinguishes itself from sibling tools by emphasizing a specialized verification pipeline with structured SEC routes for financial claims and a grounded pipeline for others, not just general Q&A.

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 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the routing between financial and non-financial claims. It also notes this tool replaces 4-6 sequential calls. However, it does not contrast directly with near siblings like ask_pipeworx_grounded, so the 'when-not' guidance is implied rather than explicit.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping or nested roles: ask_pipeworx_beta is explicitly identical to ask_pipeworx currently, ask_pipeworx_grounded uses the same router, and polymarket_arbitrage/polymarket_edges both surface mispricings. ai_visibility_check and scan_competitor_ai_presence are also tightly coupled, making tool selection error-prone.

Naming Consistency3/5

Names are uniformly snake_case and mostly readable, but the pattern is mixed: verb-first names like extract_links and discover_tools coexist with noun-first product names like polymarket_edges and entity_profile, plus bare verbs like remember and forget. This breaks the predictable verb_noun convention.

Tool Count2/5

34 tools is far too many for a server named Htmltext, and most tools are unrelated to HTML processing. Even as a broad data-research server, the count exceeds the usual 3-15 sweet spot and includes meta-tools, near-duplicate query modes, and niche utilities that bloat the surface.

Completeness3/5

The set is unusually broad—covering data research, prediction markets, memory, subscriptions, HTML extraction, AI visibility, and package scanning—but no single domain is fully fleshed out. HTML tools only do extraction, prediction-market tools lack a simple market browser, and there is no general web fetch tool. Most gaps can be worked around via ask_pipeworx, but the surface feels like a grab bag rather than a cohesive lifecycle.

Resources