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Glama

Arcgis Glasgow

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

A4.6/5.0
Behavior5/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description goes far beyond that by disclosing the two-pipeline architecture (SEC EDGAR/XBRL fast path vs. grounded fallback), the exact verdict vocabulary, citation behavior, and the critical error semantics for could_not_verify ("the check did not happen... must not be shown as one"). This is exemplary behavioral disclosure.

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 every section serves a purpose: user-phrasing triggers, routing logic, verdict semantics, and caller warnings. It is front-loaded with examples and clearly structured, though a couple of phrases (e.g., "natural-language claim verification against authoritative sources") are slightly repetitive. Dense but efficient overall.

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 thoroughly explains what callers receive: the verdict list, actual value with citation, and reasoning. It also covers the nuanced failure modes (could_not_verify vs. unsupported) and the routing decision for company-financial vs. other claims. Given the tool's complexity, this is a complete and well-rounded specification.

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% for both parameters, and the description adds real meaning with concrete claim examples and explains the tolerance_pct default and its purpose ("set 1–2 for hallucination detection"). While the schema already describes the parameters well, the description's added context about tolerance overriding claim wording and the fast-path delta math provides extra value beyond the schema.

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 explicit natural-language triggers ("Is it true that…", "fact check") and states its core function: natural-language claim verification against authoritative sources. It also differentiates itself from siblings by specifying the company-financial SEC path vs. general grounded pipeline and by noting it replaces 4–6 sequential calls, making the tool's role unmistakable.

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 clearly states when to use the tool ("Use whenever the agent needs to check whether something a user said is factually correct") and explains routing rules for financial vs. other claims. It also gives important caller guidance about verdict meanings (could_not_verify vs. unsupported). However, it doesn't explicitly name a sibling tool as an alternative or state when NOT to use this tool, so it's not quite 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

A3.6/5.0
Disambiguation2/5

Several tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (currently functionally identical), ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying catalog with only subtle differences. The Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also substantially overlaps in purpose, and the two unrelated domains (GIS vs. data/betting) make it worse.

Naming Consistency3/5

Names are readable and form some predictable clusters (polymarket_* prefix, ask_pipeworx_* suffixes, subscribe/unsubscribe/list_subscriptions), but conventions are mixed: bare verbs (remember, forget, recall), noun_noun (layer_info, entity_profile, pipeworx_feedback), verb_noun (query_layer, search_datasets), and adjective_noun (deep_research, recent_alerts). No single pattern dominates, though nothing is chaotic.

Tool Count2/5

34 tools is well above the 25+ threshold for a heavy surface, and the count is not justified by the server's stated identity: only 3 of 34 tools (search_datasets, layer_info, query_layer) relate to ArcGIS Glasgow. The remaining 31 tools belong to several unrelated domains (Pipeworx data querying, Polymarket betting, AI visibility, memory, npm scanning), making the effective scope far too broad.

Completeness2/5

For the server's named ArcGIS Glasgow domain, the surface is thin: search, schema inspection, and query are present, but there is no way to list all datasets, no spatial querying, and no write/update capability. Meanwhile the 31 non-GIS tools create a sprawling second server's worth of functionality, so the set as a whole has no coherent domain whose coverage can be judged complete.

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