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Statfin Fi

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?

The description adds critical behavioral context beyond the annotations: 'could_not_verify means the check did not happen... must not be shown as one' and distinguishes it from 'unsupported'. It also discloses the internal routing (SEC vs. grounded) and the exact percent-delta math, which helps the agent interpret results correctly and safely.

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 sentence contributes: examples, use case, routing logic, return values, and important caveats. It is front-loaded with the tool's purpose and key examples, but there is some redundancy (e.g., the initial natural-language examples could be condensed). Overall well-structured and informative.

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?

With no output schema, the description thoroughly explains return values (verdict types, actual value, citation, reasoning) and edge cases (could_not_verify vs. unsupported). It also covers the two processing paths and performance benefit, making it complete for an agent to invoke and interpret results even without a schema.

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, but the description adds meaning beyond the schema by explaining the claim verification workflow (e.g., 'exact percent-delta math' for financial claims, 'grounded pipeline' for others). It does not repeat the schema parameter descriptions, and the tolerance parameter semantics remain primarily in the schema, hence not a 5.

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 claim verification with verbs like 'fact check' and 'verify the claim that', and distinguishes it from siblings by scoping to factual claim validation with a verdict. It also differentiates the SEC/XBRL fast path for financial claims versus a grounded pipeline for other claims, which sets it apart from general ask/query 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?

The description states 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the routing to two distinct pipelines (SEC vs. grounded), giving clear when-to-use guidance. It does not explicitly name sibling alternatives but notes it 'Replaces 4–6 sequential calls', providing a comparative alternative, though an explicit 'do not use for X' is absent.

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

A4/5.0
Disambiguation3/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly the same routing purpose (beta explicitly 'currently matches ask_pipeworx exactly'), and the five polymarket_* tools plus bet_research create a dense cluster an agent must pick through. The descriptions are unusually detailed and do differentiate them, but the boundaries between the ask_pipeworx variants and between bet_research/polymarket_edges/arbitrage remain easy to misselect.

Naming Consistency4/5

All names are lowercase snake_case and mostly follow verb_noun or domain-prefix patterns (ask_pipeworx, polymarket_edges, list_subscriptions, resolve_entity). Minor deviations exist: subjects and table_meta are bare nouns rather than verbs, the ask_pipeworx family uses an ask_ prefix while the closely related deep_research does not, and entity appears as both a prefix (entity_profile) and a suffix (resolve_entity).

Tool Count2/5

34 tools is well above the 25-tool threshold for 'too many,' and the mismatch is sharpened by the server name 'Statfin Fi': only 3 of 34 tools (query_table, subjects, table_meta) actually relate to Statistics Finland, while the rest are a sprawling multi-domain platform covering prediction markets, AI visibility, npm packages, memory, and subscriptions. The count is appropriate for a general data platform but not for the apparent StatFin scope, making the surface feel bloated and unfocused.

Completeness4/5

The platform covers the full research lifecycle: discovery (discover_tools, suggest_questions), identifier resolution (resolve_entity), lookups (ask_pipeworx, entity_profile, compare_entities), verification (validate_claim, ask_pipeworx_grounded), monitoring (subscribe, recent_alerts, recent_changes), and memory (remember/recall/forget), with no obvious dead ends. Minor gaps exist — there is no keyword search across the StatFin catalog (browse-only via subjects), and one-off tools like generate_llms_txt and scan_dependency feel bolted on rather than part of a coherent domain.

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