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Macvendors

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description adds critical behavioral nuance: the distinction between 'could_not_verify' (failure to check, not evidence) and 'unsupported' (no source exists), the routing logic (structured vs grounded), and the return contract (verdict, actual value, citation, reasoning). This is high-value context not inferable from annotations.

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 longer than average, every sentence earns its place. It front-loads with colloquial triggers, then layers technical behavior (two paths, return values, error semantics) in a logical order. The 'IMPORTANT for callers' callout is well-placed. 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?

Given the tool's complexity (dual routing, special verdict meanings, no output schema), the description is complete: it explains the return structure, error handling, source coverage, and the efficiency benefit. The absence of an output schema is compensated by explicit verdict and citation descriptions. Nothing critical is missing.

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?

The input schema covers 100% of parameters, so the baseline is 3. The description adds meaningful details: it explains the tolerance_pct override for hallucination detection, states the default (implied by wording, capped at 5), and gives a concrete usage scenario. This goes beyond the schema's bare 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 a clear list of natural-language triggers ('Is it true that...', 'fact check', etc.) and states the tool performs 'natural-language claim verification against authoritative sources.' This specific verb+resource combination distinguishes it from sibling Q&A tools, and the mention of replacing 4–6 sequential calls clarifies its unified purpose.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates internal paths: company-financial claims via SEC EDGAR/XBRL, and all other factual claims via a grounded pipeline. It does not explicitly mention when NOT to use this tool versus siblings like ask_pipeworx_grounded, but the primary trigger is clear.

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 heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with the beta variant currently identical to stable. The five polymarket_* tools plus bet_research also blur boundaries, and discover_tools/suggest_questions both serve discovery. Detailed descriptions help, but an agent could easily select the wrong tool.

Naming Consistency3/5

All names use lower_snake_case and many follow a clear verb_noun pattern (resolve_entity, validate_claim, generate_llms_txt). However, there are notable deviations: entity_profile, pipeworx_feedback, polymarket_edges, recent_changes, and bet_research lead with nouns or adjectives. The style is readable and predictable in clusters, but not uniform.

Tool Count2/5

32 tools is well above the 25+ threshold for 'too many', and the server name Macvendors suggests a narrow MAC-lookup service, yet most tools belong to a much broader Pipeworx data/prediction-market platform. Several tools are near-duplicates or micro-variants (three ask_pipeworx versions, six polymarket tools). The set would be more appropriately split or heavily consolidated.

Completeness4/5

Within the actual broad research platform domain, the surface is fairly complete: discovery, routing, grounded answers, entity profiles, comparisons, recent changes, claim validation, semantic search, prediction-market analysis, subscriptions, memory, and feedback are all covered. Minor gaps exist, such as no subscription update tool and no batch MAC lookup, but agents can generally complete workflows without dead ends.

Resources