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Glama

Arcgis Carlsbad

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?

Beyond the annotations (readOnlyHint=true, openWorldHint=true, idempotentHint=true), the description adds substantial behavioral context: the automatic routing between a structured SEC EDGAR fast path and a grounded pipeline, the meaning of each verdict, and a critical warning that 'could_not_verify' means the check did not happen and must not be presented as evidence. This goes well beyond what annotations convey.

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 detailed but every sentence earns its place. It is front-loaded with user-friendly examples, then explains the two-path pipeline, the return structure, and a critical caller warning. The structure is logical and avoids wordiness.

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 correctly explains the return values: verdict list, grounded/structured value with citation, and reasoning. It also clarifies ambiguous verdicts (could_not_verify vs. unsupported) and notes that it replaces 4–6 sequential calls. For a tool of this complexity, the description is fully complete.

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 parameters are already well documented. The description adds extra meaning for `tolerance_pct`, explaining that it overrides the tolerance implied by the claim wording and suggesting values (1–2%) for hallucination detection. This enhances the schema description without redundancy.

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 identifies the tool's purpose with a specific verb ('validate') and resource ('claim'), anchored by multiple natural-language query examples ('Is it true that…', 'fact check', 'verify the claim that…'). It distinguishes this from general Q&A tools by specifying the verdict-based output and the two distinct verification pipelines (SEC EDGAR structured path vs. grounded fallback).

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 states when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains how different claim types are routed internally. However, it does not explicitly mention when not to use it or name alternative sibling tools, so it falls short of the top score.

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

Several tools have overlapping purposes, e.g., multiple 'ask' tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple 'compare' tools (compare_entities, scan_competitor_ai_presence). Descriptions are verbose but often don't clearly distinguish when to use each, causing confusion.

Naming Consistency1/5

Naming is highly inconsistent: snake_case (ai_visibility_check), camelCase (polymarket_fill_risk), and arbitrary prefixes (scan_, generate_, etc.). No consistent verb_noun pattern; e.g., 'query_layer' vs 'search_datasets' vs 'layer_info' all involve data retrieval but use different patterns.

Tool Count2/5

33 tools is excessive for a server purportedly focused on ArcGIS Carlsbad. Many tools are unrelated (e.g., polymarket betting, npm package scanning). The core ArcGIS functionality could be covered by 3-5 tools, but the server is bloated with Pipeworx utilities.

Completeness3/5

The ArcGIS portion lacks update/delete capabilities and is limited to querying. The Pipeworx tools cover a broad range of data sources but introduce many dependencies and meta-tools, creating a cluttered surface with dead ends (e.g., tools requiring paid accounts without fallback).

Resources