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Glama

Bpstat Pt

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

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description adds crucial operational behavior: it distinguishes 'could_not_verify' from 'unsupported', warns that 'could_not_verify' must not be shown as evidence, and details the two-path routing logic. This is rich, non-obvious context that helps callers interpret results correctly.

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 well-structured and every sentence earns its place: trigger examples, usage rule, routing, return payload, and a critical caveat. It is compact for the complexity it covers and front-loads the core purpose.

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 compensates by explaining the verdict list, actual value with citation, reasoning, and the meanings of error verdicts. It also covers both claim categories and the routing logic, making it complete for an agent to invoke and interpret the tool correctly.

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 fully describes both parameters, including the 'claim' example and tolerance_pct semantics. The description does not add parameter-level detail beyond mentioning 'exact percent-delta math' for the financial path, so it meets only the baseline for full schema coverage.

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 names a specific verb ('validate') and resource ('natural-language claim verification'), along with trigger phrases like 'fact check' and 'verify the claim that'. It clearly defines the tool's scope and explains the two processing paths (structured SEC EDGAR for company-financial claims, grounded pipeline for all others), distinguishing it from sibling 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 explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and provides routing rules for different claim types. However, it does not explicitly mention when NOT to use the tool or name alternative tools, so it falls 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

A3.8/5.0
Disambiguation2/5

Multiple tool clusters are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly documented as currently identical, scan_competitor_ai_presence is a thin wrapper over ai_visibility_check, and the five polymarket_* tools (edges, edge_tracker, arbitrage, fill_risk, kalshi_spread) share heavily overlapping edge-detection/fill-analysis concerns. entity_profile and recent_changes also both fan out to the same SEC/news/patents sources, so an agent must read full descriptions to avoid misselection.

Naming Consistency3/5

The dominant pattern is imperative verb_first (list_datasets, get_dataset, resolve_entity, validate_claim, scan_dependency, subscribe), but there are notable deviations: noun-phrase names like entity_profile, recent_alerts, recent_changes, bet_research, and deep_research; the ask_pipeworx brand family sits awkwardly beside get_/search_ verbs; and the polymarket_* prefix family forms yet another convention. Names are readable and mostly self-explanatory, but no single consistent scheme is followed.

Tool Count2/5

At 35 tools, this exceeds the 'too many (25+)' threshold, and the bloat is compounded by the server's nominal identity: despite being named 'Bpstat Pt' (Banco de Portugal statistics), only about 5 tools (list_domains, list_datasets, get_dataset, get_series_metadata) actually serve that domain. The other 30 tools span unrelated subsystems — Polymarket betting, npm dependency scanning, AI visibility audits, generic memory, and subscriptions — suggesting several products bundled into one server.

Completeness3/5

For the broader data-gateway interpretation, the lifecycle is well covered: discovery (discover_tools, suggest_questions), query (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), deep research (deep_research), profiles/comparisons (entity_profile, compare_entities), monitoring (recent_changes, subscribe/recent_alerts), and memory (remember/recall/forget). However, for the nominal Bpstat statistical domain there is no keyword search over series or datasets — navigation requires knowing domain/dataset ids in advance — and the extreme scatter across unrelated domains leaves each subsystem only partially developed.

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