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Gets Nz

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. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description goes far beyond the readOnlyHint by detailing the two internal pipelines, the exact verdict enum, and the crucial caveat that 'could_not_verify' means the check did not happen and must not be treated as evidence. It also clarifies the 'unsupported' state, adding significant behavioral context beyond the annotations.

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 densely informative, with no filler. It front-loads trigger phrases and purpose, then efficiently covers internal routing, return values, and error semantics. It could be slightly more compact, but every sentence earns its place.

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?

Since there is no output schema, the description appropriately explains the return value structure including the verdict enum, citation, and reasoning. It also covers error and unsupported cases, making it complete for a tool of this complexity and read-only nature.

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?

Schema description coverage is 100%, and the tool description does not add substantial meaning beyond the schema. It mentions the tolerance override and natural-language claim format, but these are already fully described in the input schema, so the description's contribution is marginal.

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 clearly identifies the tool as 'natural-language claim verification against authoritative sources' and lists specific trigger phrases. It also distinguishes the financial vs. non-financial processing paths and notes it replaces multi-step pipelines, making its purpose unmistakable.

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.' It also explains the routing for financial vs. other claims, but it does not explicitly name alternative sibling tools or state when not to use this tool, leaving some room for interpretation.

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

Multiple tools overlap significantly: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly identical purposes, and deep_research further duplicates. Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) also have overlapping scopes, making it hard for an agent to select the correct one without deep inspection.

Naming Consistency3/5

Tool names use a mix of patterns: some are descriptive phrases (ai_visibility_check, generate_llms_txt), others are domain-prefixed (nz_tender_*, polymarket_*) but lack a uniform verb_noun structure. The ask_pipeworx series diverges from the rest, and verb choices are inconsistent (compare_entities vs scan_competitor_ai_presence).

Tool Count2/5

With 34 tools, the surface is large and feels bloated. The server covers multiple distinct domains (NZ tenders, Polymarket, memory, subscriptions) that could be separate servers. Many tools are variants of the same core functionality (e.g., four ask_pipeworx variants), inflating the count without clear necessity.

Completeness3/5

For a general-purpose data query server, the tool set covers a broad range of sources (SEC, FDA, FRED, etc.) and includes CRUD for memory and subscriptions. However, obvious gaps exist: no dedicated web search tool (ask_pipeworx is for structured data), and NZ coverage is limited to tenders only. Missing update/delete for some resources (e.g., no way to modify a subscription beyond unsubscribe).

Resources