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Glama

Nhl

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. Added

TDQS

A4.5/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing the critical distinction between 'could_not_verify' (a failed check, not evidence) and 'unsupported' (looked but no source covered), plus the internal routing between SEC/XBRL fast path and the grounded pipeline. It also explains the return payload (verdict, value, citation, reasoning) and the error payload with verification_error{stage,detail}, which is highly valuable behavioral context.

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 the description is long, it is tightly structured and every sentence carries essential information: trigger phrases, usage scope, pipeline details, verdicts, and a high-importance caveat about could_not_verify. It is front-loaded with the most actionable phrases and uses clear prose, so the length is justified.

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?

Even without an output schema, the description specifies all verdict types, the actual value with citation, reasoning, error structure, and the distinction between could_not_verify and unsupported. It covers the two major routing paths (financial vs. other claims) and states what the tool replaces, making it sufficiently complete for high-complexity use.

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?

The input schema already provides 100% parameter coverage with detailed descriptions, including tolerance_pct semantics and default behavior. The description itself does not add new parameter-level meaning beyond reinforcing that tolerance is claim-dependent; it earns the baseline score for well-documented schema but no extra credit.

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 states this is 'natural-language claim verification against authoritative sources' and provides explicit trigger phrases like 'fact check' and 'verify the claim that...'. It distinguishes itself from sibling tools by emphasizing it is a single-call replacement for NL parsing → entity resolution → data lookup → comparison, and by scoping to checking whether something a user said is factually correct.

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,' giving clear context for invocation. However, it does not name alternative tools to consider when this tool is not appropriate (e.g., ask_pipeworx for open-ended queries), so it stops short of explicit when-not guidance.

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
Disambiguation2/5

The set contains multiple near-overlapping query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools) and a cluster of prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) with fuzzy boundaries. ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, making misselection likely.

Naming Consistency2/5

Naming is mixed across clusters: get_* for NHL tools, ask_pipeworx* for queries, polymarket_* for prediction markets, bare verbs (remember, recall, forget), and noun_verb phrases (entity_profile, recent_changes, scan_dependency). Each cluster is internally consistent, but there is no unifying pattern across the server.

Tool Count1/5

35 tools on a server named 'Nhl' is an extreme mismatch: only 4 tools actually relate to NHL data, while the other 31 are a general-purpose Pipeworx data/research/prediction-market platform. The set is not well-scoped for its apparent purpose, and even as a general data server it feels like a grab bag of unrelated capabilities.

Completeness2/5

For the NHL domain the surface is thin: player, schedule, scores, and standings, but no team info, roster lookup, player search by name, or game/play-by-play details. The Pipeworx side is more complete but fills the server with functionality unrelated to the NHL branding, so the overall surface is fragmented and leaves obvious domain gaps.

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