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Hts

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/idempotent/non-destructive, and the description adds significant behavioral context: it defines the verdict set, explains that 'could_not_verify' means the check did not happen and must not be treated as evidence, and clarifies 'unsupported' versus 'could_not_verify'. This goes well beyond the annotations and helps the agent reason about edge cases.

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 front-loaded with trigger phrases and packed with necessary routing and error-semantics details. Each sentence contributes, though the 'Replaces 4–6 sequential calls' note is somewhat extraneous to core usage and could be trimmed without losing essential 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 having no output schema, the description thoroughly covers what the tool returns (verdicts, cited actual value, reasoning) and explains ambiguous outcomes like could_not_verify and unsupported. Given the tool's complexity and the two distinct routing paths, this is a complete and self-sufficient description.

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 baseline is 3. The description adds extra meaning for tolerance_pct by explaining it overrides wording-implied tolerance and is useful for hallucination detection, and it clarifies the 'exact percent-delta math' used for financial claims. This elevates parameter understanding 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 opens with natural-language triggers and a clear verb+resource statement: 'natural-language claim verification against authoritative sources.' It distinguishes itself by specifying two routing paths (SEC EDGAR for company-financial claims, grounded pipeline for all other facts) and explicitly says it replaces 4–6 sequential calls, making its unique role obvious.

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 an explicit usage signal: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also gives clear context for the two claim types (financial vs. other), but it does not explicitly name alternative tools or say when not to use it, so it falls short of a full when/when-not/alternatives articulation.

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 overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same 5,529-tool catalog, with ask_pipeworx_beta currently identical to ask_pipeworx. The Polymarket suite also has ambiguous boundaries (bet_research vs. polymarket_edges vs. polymarket_arbitrage), and scan_competitor_ai_presence simply wraps ai_visibility_check, so agents may struggle to choose the right tool.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes verb-led names (ask_pipeworx, compare_entities, scan_dependency, validate_claim, hts_search) with noun-led names (entity_profile, recent_alerts, polymarket_edges, ai_visibility_check). Variant suffixes like ask_pipeworx_beta / ask_pipeworx_grounded add further inconsistency, so the naming is coherent enough but not predictable enough for a 4.

Tool Count2/5

At 33 tools, the server exceeds the 25-tool threshold for 'too many' in the rubric. Even though the broad data-research scope justifies a larger surface, the count feels bloated because several tools are near-duplicates (e.g., ask_pipeworx_beta, ask_pipeworx_grounded) or wrappers (scan_competitor_ai_presence), making the set heavier than necessary.

Completeness4/5

The server covers its core domains thoroughly: data querying (ask_pipeworx family, deep_research), entity resolution and comparison (resolve_entity, entity_profile, compare_entities), verification (validate_claim, ask_pipeworx_grounded), HTS tariff lookup (search + detail), subscription lifecycle (subscribe/unsubscribe/list/recent_alerts), and memory (remember/recall/forget). Minor gaps exist, such as no subscription-update endpoint and no generic raw-source export, but these are workaround-able and do not cause agent failures.

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