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Glama

Ilostat

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 readOnly, openWorld, idempotent, and non-destructive. The description adds rich behavioral context beyond this: explains the structured vs. grounded routing, discloses the meaning of 'could_not_verify' (not evidence either way), and clarifies the 'unsupported' verdict. This is exactly the kind of supplementary context that makes the tool safe to invoke.

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 each sentence carries critical operational detail, especially the 'IMPORTANT for callers' section distinguishing error outcomes. The use of dashes and clear sectioning makes it scannable, though a slight tightening of wording would push it to a 5.

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?

Despite the lack of an output schema, the description fully explains the verdict values, the two processing paths, the meaning of every outcome, and the error semantics. It covers usage, edge cases, and externally observable behavior comprehensively for a tool of this complexity.

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% and both parameters are already described in the schema. The description adds value by providing concrete examples for the 'claim' parameter and explaining how 'tolerance_pct' overrides the claim-wording-implied tolerance and its use for hallucination detection. This goes beyond mere schema repetition.

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 as a claim verification tool with specific verbs like 'fact check' and 'verify the claim that'. It distinguishes itself by detailing the two processing pathways (SEC EDGAR fast path and grounded pipeline) and explicitly states it replaces multiple sequential calls, making its unique role clear among siblings.

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?

Provides explicit guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates when the fast path vs. grounded pipeline is used. However, it does not explicitly name alternative sibling tools or state 'when not to use', so it stops 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

A3.9/5.0
Disambiguation2/5

The server mixes three near-identical ask_pipeworx variants (stable, beta, grounded) where beta is currently described as functionally identical to stable, plus several overlapping discovery and research tools (discover_tools, suggest_questions, deep_research, validate_claim, ask_pipeworx). Multiple entity/comparison/change tools (entity_profile, compare_entities, recent_changes) and several Polymarket tools further blur boundaries, requiring careful reading of long descriptions to pick correctly.

Naming Consistency3/5

Many tools follow a clear verb_noun snake_case pattern (list_dataflows, get_data, compare_entities, validate_claim, resolve_entity), and the polymarket_* prefix groups the prediction-market family consistently. However, naming is mixed: bare verbs (remember, forget, recall), noun phrases (dataflow_structure, entity_profile), brand-prefixed tools (pipeworx_feedback, pipeworx_trending), and inconsistent verb choices like ask_pipeworx vs ask_pipeworx_grounded vs suggest_questions.

Tool Count2/5

34 tools is heavy for a server named Ilostat, especially since only three tools (list_dataflows, dataflow_structure, get_data) actually serve ILOSTAT data. The rest form a broad general-purpose data/prediction-market platform that appears bolted on rather than scoped to the server's stated identity.

Completeness4/5

For the ILOSTAT domain specifically, the read-only lifecycle is complete: list_dataflows discovers datasets, dataflow_structure explains dimensions/codes, and get_data retrieves observations — no obvious dead ends for public data access. Other embedded subsystems (memory, subscriptions) also have full CRUD, though the overall server lacks a coherent single-domain surface to judge against.

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