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Glama

flood

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

A4.4/5.0
Behavior5/5

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

Annotations only indicate read-only, open-world, idempotent, and non-destructive. The description adds crucial behavioral context: the distinction between could_not_verify (check didn't happen, must not be used as evidence) and unsupported (no source found), plus the existence of verification_error with stage/detail. This is genuinely useful beyond 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 longer than ideal but front-loaded with query pattern examples and ends with an important caller-specific warning. Every sentence adds meaningful information; the length is justified by the tool's complexity.

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?

No output schema exists, so the description compensates by listing the full verdict set, citation format, reasoning output, and error semantics. It also explains the fallback routing logic and the distinction between could_not_verify and unsupported, making it complete for a claim verification tool.

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 coverage is 100% and both parameters already have rich descriptions covering tolerance override and default behavior. The tool description adds only marginal color like 'exact percent-delta math' and 'capped at 5', which is already in the schema. No significant added value.

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 a specific verb+resource: verifies natural-language claims against authoritative sources. It distinguishes itself from sibling tools by listing trigger phrases, describing the financial fast path vs grounded fallback, and noting 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 Guidelines4/5

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

Clearly states 'Use whenever the agent needs to check whether something a user said is factually correct' and gives detailed routing logic for company-financial vs other claims. It doesn't explicitly name alternative tools or when-not-to-use, but the 'replaces sequential calls' implies a preferred single-call approach.

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

Several tool clusters have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all analyze prediction-market opportunities; discover_tools and suggest_questions both serve as meta-tool onboarding. An agent could easily pick the wrong variant.

Naming Consistency3/5

Most tools use snake_case, but naming patterns are mixed: some are verb_noun (get_flood_forecast, list_subscriptions), some are noun-centric (entity_profile, bet_research), some are bare verbs (remember, recall, forget), and some use long descriptive phrases (ask_pipeworx_grounded, polymarket_kalshi_spread). It is readable but lacks a single predictable convention.

Tool Count2/5

33 tools is heavy for a server whose apparent core is flood forecasting — only 2 of 33 tools (get_flood_forecast, get_river_discharge) relate to flooding. The bulk is a sprawling Pipeworx data-access, prediction-market, and memory layer, making the surface feel overstuffed and off-topic relative to the server name.

Completeness2/5

If the intended domain is flood data, the surface is severely incomplete: only forecast and discharge lookups exist, with no historical flood events, alert subscriptions, mapping, or severity-warning tools. If instead the domain is meant to be Pipeworx-style data research, the surface is broad but still has gaps (no direct SEC filing text retrieval, no clear update/delete lifecycle for many resources). Either way, the purpose is unclear and coverage is mismatched.

Resources