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Glama

Ipquery

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?

The description goes well beyond the annotations by explaining the two-path routing (SEC EDGAR + XBRL vs. grounded pipeline), the exact verdict vocabulary, and the critical nuance that could_not_verify does not constitute evidence. It also clarifies the meaning of unsupported. No contradiction with readOnlyHint/openWorldHint/idempotentHint is present.

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?

Though the description is several sentences long, it is tightly structured and front-loaded with trigger phrases and purpose. Each section (routing, verdicts, caller warnings) earns its place; there is no fluff. The length is appropriate for 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?

With no output schema, the description carries the full burden of explaining return values. It enumerates all possible verdicts, mentions the grounded actual value with citation, and describes reasoning. It also covers error semantics (verification_error with stage/detail) and the distinction between could_not_verify and unsupported, making it fully complete for an agent.

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% coverage for both parameters, including detailed behavior for tolerance_pct. The description adds only a passing reference to 'exact percent-delta math' for the financial path, which does not meaningfully augment the schema's documentation. Baseline of 3 is appropriate.

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 the tool's purpose: 'natural-language claim verification against authoritative sources' with specific trigger phrases like 'fact check' and 'verify the claim that…'. It explicitly contrasts this with siblings by focusing on claim verification and returning verdicts, and no sibling tool offers the same functionality.

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?

It gives an explicit when-to-use directive: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also details the internal routing for company-financial vs. other claims and notes it replaces 4–6 sequential calls, providing clear context for when this tool is appropriate.

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
Disambiguation3/5

Many tools are clearly distinct, but the existence of multiple ask_pipeworx variants (standard, beta, grounded) and the overlapping check_risk/lookup_ip tools create meaningful selection ambiguity. Description differentiation helps but doesn't fully resolve it.

Naming Consistency3/5

All names use snake_case, but conventions vary between verb-noun (lookup_ip, compare_entities), noun phrases (entity_profile, recent_alerts), and domain-prefixed names (polymarket_edges, pipeworx_trending). It's readable but not a consistent pattern.

Tool Count2/5

At 33 tools the surface is large, with duplicated ask_pipeworx variants, a modest IP lookup core, and a large number of meta/management tools (memory, subscriptions, onboarding, feedback). Even with a broad scope this feels overloaded, and it is well above the 25+ threshold.

Completeness4/5

The query domain is well covered: entity profiles, comparison, claim validation, deep research, changes, plus memory and subscription management. Minor gaps exist (no direct subscription update, no raw citation fetch utility), but agents can work around them.

Resources