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Glama

Arcgis Dukescounty

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 execution paths (SEC EDGAR fast path and grounded pipeline), the return verdicts, the meaning of could_not_verify vs. unsupported, and the presence of citations and reasoning. It also discloses failure modes via verification_error, providing rich 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.

Conciseness5/5

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

Although long, every sentence earns its place. The description is front-loaded with trigger phrases and purpose, then efficiently details routing, return values, caveats, and the efficiency benefit. It is well-structured and free of redundant information.

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 moderate complexity (2 params, no output schema), the description fully compensates by explaining the return values, verdict meanings, error semantics, and examples. It covers edge cases and provides enough detail for an agent to invoke it correctly without additional context.

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%, so the baseline is 3. The description adds meaningful context beyond the schema, particularly for tolerance_pct: it explains how the default is derived from claim wording, how to override it for hallucination detection, and the range constraint. The claim parameter is further illustrated with examples, adding value.

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 explicit trigger phrases like "fact check" and "verify the claim that." It distinguishes itself from sibling tools by focusing on verifying factual claims and returning verdicts, rather than general Q&A or 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 explicitly says "Use whenever the agent needs to check whether something a user said is factually correct," and explains the routing for company-financial vs. other claims. It does not name alternative tools, but the "Replaces 4–6 sequential calls" statement implies a comparison to a manual multi-step workflow. It lacks an explicit 'when not to use' clause, so a 4 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

A3.7/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes that require reading long descriptions to separate. The server name promises ArcGIS Dukes County but most tools are unrelated, making the overall selection space confusing.

Naming Consistency3/5

Most names are snake_case, but conventions vary: some are verb_noun (query_layer, search_datasets, list_subscriptions), some noun-ish (entity_profile, layer_info, pipeworx_trending), some bare verbs (remember, recall, forget, subscribe), and some long compounds (polymarket_edge_tracker, scan_competitor_ai_presence). Readable overall, but no strong consistent pattern.

Tool Count2/5

34 tools is heavy, and only three (search_datasets, layer_info, query_layer) relate to the server's apparent ArcGIS Dukes County purpose. The rest form a sprawling Pipeworx/prediction-market/memory/utility toolkit, creating a severe scope mismatch with the server's name.

Completeness2/5

For the named Dukes County GIS domain, the surface is minimal—dataset discovery, schema, and queries—with no spatial operations or editing, though read-only access may be acceptable. For the broader Pipeworx functionality it is fairly comprehensive, but that is not what the server name advertises, leaving the set incomplete relative to its apparent identity.

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