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Arcgis Johnsoncity

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.

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses critical behaviors: the SEC EDGAR fast path, grounded-pipeline fallback, and the exact semantics of verdicts. It especially clarifies the distinction between could_not_verify (system failure, not evidence) and unsupported (no source), which is essential for correct agent behavior.

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 dense but every sentence serves a purpose: examples, routing logic, verdict semantics, error handling, and efficiency gain. It is well-organized and front-loaded with the most actionable trigger phrases, making it easy for an agent to parse and apply.

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 the tool's complexity (multiple verdicts, two execution paths, error cases), the description covers all key aspects: return values, citations, reasoning, and the crucial caller instruction about could_not_verify. Even without an output schema, the agent has enough context to use the tool correctly.

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?

Schema coverage is 100%, but the description adds meaningful context: claim is the natural-language input with examples, and tolerance_pct's default and override semantics are explained, including a practical use case (set 1–2 for hallucination detection). This enriches the schema beyond plain parameter names.

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 identifies a specific verb+resource: claim verification against authoritative sources, with natural-language examples and a clear deliverable (verdict). It distinguishes itself from sibling tools by framing the task as fact-checking with structured verdicts and a two-path pipeline.

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?

The description explicitly states when to use it: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing logic for company financial claims vs. other claims, and notes that it replaces 4–6 sequential calls, effectively contrasting with alternative approaches.

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

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to similar data sources, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The many polymarket tools also blur together despite detailed descriptions.

Naming Consistency3/5

Most names are snake_case, but there is no consistent verb_noun pattern: ask_pipeworx, bet_research, entity_profile, layer_info, pipeworx_trending, recent_changes, and validate_claim follow different stylistic conventions. The pattern is readable but feels like several naming vocabularies were merged.

Tool Count2/5

34 tools is well over the comfortable range and most of them are unrelated to the apparent ArcGIS Johnson City purpose. Only search_datasets, layer_info, and query_layer actually serve GIS needs; the remaining 31 tools form a sprawling Pipeworx meta-platform bolted onto the same server.

Completeness3/5

The ArcGIS portion covers discover-schema-query reasonably well for read-only open data, and the Pipeworx side has broad coverage with subscriptions, memory, feedback, and research workflows. However, the surface is defined by two unrelated domains, making it hard to judge true completeness for any one stated purpose.

Resources