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airports

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

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and open-world behavior; the description adds substantial context beyond these: the two pipeline paths (SEC EDGAR/XBRL for financial vs grounded pipeline for others), the full verdict taxonomy, and the crucial warning that could_not_verify is a failed-check signal, not evidence for/against the claim, while unsupported means no source coverage. This prevents serious misinterpretation of results.

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?

The description is information-dense yet well-organized: trigger phrases, core purpose, usage condition, pipeline behavior, return values, and a critical caller caveat. Every sentence earns its place, and the structure front-loads the most important operational detail early for agent guidance.

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 fully covers the return contract: verdict values, grounded/structured actual value with pipeworx:// citation, and reasoning. It also explains the two routing paths and failure semantics, making the tool self-contained for an agent to invoke and interpret results correctly. No important gap remains.

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% for both parameters, so the baseline is 3. The description adds value by explaining that tolerance_pct overrides the claim-wording default, is capped at 5, and should be set to 1–2 for hallucination detection. It also provides claim examples that illustrate what natural-language claims look like, aiding parameter selection.

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 explicitly states the tool's function: natural-language claim verification against authoritative sources, with concrete trigger phrases like "fact check" and "verify the claim that…". It clearly distinguishes itself from sibling research/search tools by focusing on verdict generation for factual claims, not open-ended research.

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 gives clear usage context: "Use whenever the agent needs to check whether something a user said is factually correct." It also explains the routing between financial and non-financial claims and notes it replaces multi-step manual verification. However, it lacks explicit external alternatives or when-not-to-use conditions, so it stops short of full exclusion 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

A3.5/5.0
Disambiguation2/5

Many tools have distinct purposes, but the three 'ask_pipeworx' variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are highly similar and likely cause confusion. Additionally, the toolset mixes airport-specific tools with unrelated financial and research tools, creating ambiguity about when to use which.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use descriptive snake_case (ai_visibility_check, ask_pipeworx), others are single verbs (remember, forget, recall), and some include brand names (pipeworx_feedback). No clear pattern emerges across the 34 tools.

Tool Count1/5

34 tools is excessive for a server named 'airports'—only 3 tools directly relate to airports (search_airports, get_airport, calculate_distance). The majority are unrelated utilities (financial, prediction markets, memory), making the scope far too broad and unfocused.

Completeness1/5

The server severely lacks completeness for its stated airport domain: there are no tools for flights, airlines, runways, or real-time data. The other included domains (e.g., financial, prediction markets) are also incomplete, with e.g., only partial coverage of company data and no update/delete operations.

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