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Glama

Arcgis Fairfield

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

A4.4/5.0
Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description reveals the verdict vocabulary, the critical caveat that could_not_verify indicates the check didn't happen (with verification_error) and must not be used as evidence, the difference between unsupported and could_not_verify, and the two pipeline paths. This is substantial 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 front-loaded with trigger phrases and structured logically from purpose to routing to returns to caveats. Every sentence carries useful information, but it is fairly long. It earns a 4 because the density is warranted by the tool's complexity and the important error-handling warning.

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?

For a tool with no output schema, the description fully specifies the input types, the routing logic, the verdict enum, the return payload (value + citation + reasoning), and the meaning of each error state (could_not_verify vs unsupported). It also mentions efficiency gains. No significant gaps remain.

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?

The input schema already provides 100% coverage with detailed descriptions for both claim and tolerance_pct, including examples and default behavior. The description does not add parameter-specific semantics beyond the schema; it only mentions percent-delta math at a high level. Baseline 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 opens with trigger phrases ('fact check', 'verify the claim that…') and explicitly states it performs natural-language claim verification against authoritative sources. It distinguishes its coverage between company-financial claims (SEC EDGAR/XBRL path) and all other factual claims (grounded pipeline), and notes it replaces 4–6 sequential calls, clearly differentiating it from sibling research tools.

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?

It gives an explicit usage condition: '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. However, it does not name alternative tools or provide when-not-to-use exclusions, so it falls just short of a 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

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, deep_research, and also multiple Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage). Descriptions try to differentiate but the boundaries are unclear, causing confusion.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ask_pipeworx, query_layer), some use descriptive phrases (entity_profile, recent_changes), and there is no consistent verb_noun pattern. The variety makes it hard to predict tool names.

Tool Count1/5

33 tools is far too many for a server named 'Arcgis Fairfield', as most tools are unrelated to GIS or Fairfield (e.g., npm dependency checks, prediction markets, AI visibility). The tool count severely mismatches the server's purported scope.

Completeness1/5

The server's stated purpose is ArcGIS Fairfield, but only 3 tools (layer_info, query_layer, search_datasets) relate to that domain. Critical GIS operations like updating features or managing services are missing, while the vast majority of tools are for other domains.

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