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Arcgis Allegheny

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

A4.4/5.0
Behavior5/5

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

Annotations already cover read-only and idempotent, but the description adds essential behavioral context: it distinguishes 'could_not_verify' (check did not happen, must not be treated as evidence) from 'unsupported' (no source), explains the two routing paths (structured vs. grounded), and mentions error details. This goes well beyond the annotations and is critical for correct usage.

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 average but front-loaded with intent examples and essential routing/error semantics. Some redundancy exists (e.g., 'answered with verbatim evidence, then judged'), but every major section earns its place for such a complex tool.

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?

There is no output schema, so the description compensates by listing the return verdicts, citation, reasoning, and critical error semantics. It also explains the two data paths and the tolerance behavior. This is comprehensive and leaves little to guess 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?

Schema has 100% coverage with rich descriptions for both parameters (claim example, tolerance_pct range and default). The description adds no new parameter-specific meaning beyond what the schema already states, so the baseline of 3 applies.

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 against authoritative sources, using specific verbs like 'verify' and 'confirm or refute.' It provides concrete example user phrasings and distinguishes itself from generic search/research siblings by focusing on fact-checking, even mentioning it replaces 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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and lists trigger phrases. However, it does not name alternative tools or explicitly state when not to use it, so it misses the 'when-not/alternatives' portion for a full 5.

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

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, and several Polymarket analysis tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, bet_research) blur together. The three ArcGIS tools are distinct, but they are drowned out by a large set of data-query and prediction-market tools with unclear boundaries.

Naming Consistency3/5

All tool names use snake_case, but the verb/noun pattern is mixed: some are command-style (query_layer, validate_claim), some are noun phrases (layer_info, entity_profile), and others are bare verbs (remember, forget). The naming is readable but not consistently patterned.

Tool Count2/5

34 tools is too many for a server named 'Arcgis Allegheny', especially since only three tools (search_datasets, layer_info, query_layer) relate to ArcGIS at all. The bulk of the tools address unrelated domains like Pipeworx data lookups and Polymarket betting, making the count excessive for the apparent purpose.

Completeness1/5

For a server focused on ArcGIS Allegheny County data, the surface is severely incomplete: only three read-only tools (search_datasets, layer_info, query_layer) cover the domain, and they lack operations like adding, updating, or deleting features. The remaining 31 tools are unrelated to GIS, so the server fails to provide a coherent or complete toolset for its stated purpose.

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