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Gdelt

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. Added

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses critical behavioral nuances: could_not_verify means the check didn't happen and must not be interpreted as evidence, unsupported means no source was found, and it explains return structure with verdicts, citations, and reasoning. This is valuable context not available in annotations.

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 information-dense but well-structured, starting with trigger phrases, then usage, pipeline detail, return format, and a clearly marked 'IMPORTANT' caveat. It is longer than most, but every section contributes critical operational knowledge, so it earns a 4 rather than 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?

Given the tool's complexity (dual routing, multiple verdicts, error semantics) and the absence of an output schema, the description provides a thorough account: it lists all verdicts, explains the meaning of ambiguous ones, states the source paths, and notes it replaces multi-step calls. This is complete enough for an agent to invoke and interpret results 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?

Input schema covers both parameters (claim and tolerance_pct) with descriptive text, so baseline is 3. The tool description does not add additional meaning about parameters beyond what the schema already provides, so no extra credit is warranted.

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 states the tool's purpose: natural-language claim verification against authoritative sources, with specific verb 'verify claim' and examples. It distinguishes itself from siblings by describing the two routing paths (SEC EDGAR for financial, grounded pipeline for everything else) and mentions it replaces multi-step pipelines.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear context. It also details the two sub-paths (financial vs. other claims), but does not name alternative tools or state when not to use it, so it lacks explicit exclusions.

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 tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are near-duplicates, while deep_research, entity_profile, compare_entities, and bet_research all route into the same underlying Pipeworx catalog. ai_visibility_check and scan_competitor_ai_presence also overlap heavily. Descriptions are detailed, but an agent could easily misselect among the research and market-analysis clusters.

Naming Consistency3/5

All names use lowercase snake_case, but the pattern is mixed: verb-first names (search_articles, resolve_entity, validate_claim), noun-phrase domain tools (entity_profile, polymarket_fill_risk), brand-prefixed names (pipeworx_trending, ask_pipeworx), and bare verbs (recall, remember, forget). It is readable and consistent in style, but there is no predictable verb_noun convention and tool names do not reliably indicate their domain.

Tool Count2/5

With 35 tools, this set is well beyond the 3-15 well-scoped range and even the 16-25 heavy range. Several clusters could be consolidated (three ask_pipeworx variants, five polymarket analysis tools, three memory tools), and unrelated utilities like generate_llms_txt and scan_dependency add to the sprawl.

Completeness4/5

For a data-research server, coverage is broad: universal lookup, grounded answers, deep research, entity profiles, comparisons, news search/sentiment/timelines, prediction-market analysis, and memory/subscription lifecycle tools are all present. The main gaps are direct raw-document fetching (e.g., full article text or a specific SEC filing body), but the universal ask_pipeworx router and pipeworx:// resource URIs let agents work around those.

Resources