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Glama

Nps

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

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnly, openWorld, idempotent), the description discloses the full set of verdicts, the meaning of 'could_not_verify' (check did not happen) versus 'unsupported' (no source exists), and that citations use pipeworx://. This is significant behavioral context about failure modes and return semantics.

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 contributes operational guidance, and the structure flows logically from purpose to usage to return semantics to warnings. It is slightly verbose but not padded or redundant.

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 enumerates the possible verdicts, the payload (grounded or structured actual value with citation), and the error object with stage/detail. It also explains how this tool replaces multi-step workflows, making it exceptionally complete for callers.

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 value by elaborating on tolerance_pct (override behavior, recommended range for hallucination detection) and giving concrete claim examples for the 'claim' parameter, which enriches the schema's minimal 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 uses specific verbs like 'verify' and 'fact check' and names the resource as natural-language claims. It clearly distinguishes this tool from siblings by describing the two-pipeline behavior (SEC EDGAR/XBRL for company financial claims, grounded pipeline for everything else) and by stating it replaces 4–6 sequential calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides detailed routing rules (financial vs. other) and instructs callers on how to interpret 'could_not_verify' and 'unsupported,' which is essential for correct usage.

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

A3.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., NPS park tools vs. Pipeworx queries vs. Polymarket analysis). Some overlap exists between ask_pipeworx and ask_pipeworx_grounded, and among the many Polymarket tools, but descriptions help differentiate them.

Naming Consistency3/5

Naming conventions vary: some use verb_noun (list_parks), others use descriptive phrases (ask_pipeworx_grounded, scan_competitor_ai_presence). While all use snake_case, there is no consistent pattern in prefixes or verb choice.

Tool Count2/5

35 tools is too many for a coherent server. The set appears to bundle multiple unrelated domains (NPS, Pipeworx, Polymarket, memory, subscriptions) into a single server, lacking focused scope.

Completeness3/5

Each domain has notable gaps (e.g., NPS lacks real-time conditions; Pipeworx lacks data ingestion tools; Polymarket lacks order placement). Some sub-areas (memory, subscriptions) are complete, but overall the surface is incomplete for the broad range of domains.

Resources