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Glama

Delaware DMV

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

A4.7/5.0
Behavior5/5

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

The description goes far beyond the annotations by detailing the dual pipeline (SEC/XBRL structured path vs grounded pipeline), the exact verdict enum, the meaning of could_not_verify with verification_error details, and the instruction that could_not_verify is not evidence for or against a claim. It also clarifies unsupported semantics and return values, adding essential 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but nearly every sentence adds useful information. It is front-loaded with trigger phrases and examples, though the long run-on sentence covering the company-financial vs other-claim split could be more readable. Overall, it is appropriately sized 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?

Given there is no output schema, the description thoroughly explains return values: verdict options, actual value with pipeworx:// citation, reasoning, and verification_error structure. It also covers edge cases (could_not_verify, unsupported) and the benefit of replacing multiple calls, making the tool fully self-contained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While schema coverage is 100%, the description significantly enriches both parameters: claim gets realistic examples, and tolerance_pct gets precise usage guidance (overrides implied tolerance, 1–2 for hallucination detection, default capped at 5). This is value beyond the schema fields.

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 performs natural-language claim verification with explicit trigger phrases like "fact check" and "verify the claim that…". It identifies the specific resource (factual claims), the action (verify), and distinguishes itself from sibling tools by focusing on truth/falsity of claims.

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 says "Use whenever the agent needs to check whether something a user said is factually correct" and provides routing guidance (company-financial claims vs any other factual claim). It does not name specific alternative tools but effectively defines its scope with examples and exclusions for unsupported claims.

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 tools are effectively duplicates or near-duplicates: ask_pipeworx and ask_pipeworx_beta are described as currently identical, ai_visibility_check and scan_competitor_ai_presence overlap heavily, and the polymarket_arbitrage/polymarket_edges/polymarket_fill_risk cluster has fuzzy boundaries. Even within the DMV subset, de_dmv_ev_adoption and de_dmv_vehicle_registrations both answer overlapping EV-count questions.

Naming Consistency2/5

The de_dmv_* tools follow one snake_case pattern, but the rest of the set mixes bare nouns, brand-prefixed verbs, and generic names (entity_profile, remember, generate_llms_txt, ask_pipeworx_beta). There is no consistent verb_noun or domain-prefix convention across the 36 tools.

Tool Count2/5

36 tools is over the threshold where a tool set becomes hard to navigate, and most of them have nothing to do with a Delaware DMV server. Only five tools are DMV-related; the rest are a general-purpose Pipeworx data, memory, and prediction-market toolkit, which makes the set feel bloated and mis-scoped.

Completeness2/5

For a server named Delaware DMV, the surface is missing core DMV capabilities like driver licenses, vehicle titling, registration renewals, appointments, or fee lookups. The five de_dmv_* tools cover only EV adoption, rebates, charger rebates, crash stats, and registration counts, leaving obvious domain gaps.

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