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Api Ninjas

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

A5/5.0
Behavior5/5

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

Annotations already mark readOnly, openWorld, idempotent, and non-destructive. The description adds substantial behavioral detail: the dual-pipeline routing, verdict semantics (could_not_verify vs unsupported), and error structure, which are not present 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Though long, the description is front-loaded with trigger phrases and every sentence contributes operational detail (verdicts, citations, error handling). There is no redundancy or fluff for a tool with this complexity.

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 lacking an output schema, the description fully specifies return values (verdict, actual value, citation, reasoning), covers error cases (could_not_verify, unsupported), and explains the two verification paths. This is complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds operational value: tolerance_pct overrides the implied tolerance, has a default cap of 5, and is recommended set to 1–2 for hallucination detection. This goes beyond the schema's basic definitions.

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 uses a specific verb ('verify') and resource ('natural-language claim') and lists clear trigger phrases and examples. It clearly distinguishes itself from siblings by focusing on fact-checking rather than general question answering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It further partitions financial vs other claims and notes that it replaces 4–6 sequential calls, giving clear when-to-use context and an implicit alternative.

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

A4.1/5.0
Disambiguation3/5

Most tools have distinct roles, but there is meaningful overlap among the question-answering family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and among the Polymarket analysis tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk). The long descriptions help separate them, but the boundaries are still subtle enough that an agent could easily pick the wrong variant.

Naming Consistency4/5

The naming is mostly snake_case and generally follows a verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity, scan_dependency, validate_claim). Deviations like entity_profile, recent_alerts, recent_changes, and bare verbs (forget, recall, remember, subscribe, unsubscribe) are minor and do not seriously harm predictability.

Tool Count3/5

31 tools is heavy for a single server and suggests the surface is a bundled platform (data queries, prediction markets, memory, subscriptions, AI-visibility checks) rather than one tightly scoped domain. Each tool has a rational purpose, but the sheer count plus several meta/didactic tools makes the set feel somewhat oversized.

Completeness4/5

Core workflows are well covered: entity resolution, profiles, comparisons, grounded lookup, fact-checking, deep research, memory CRUD, and subscription lifecycle. Gaps are minor. There are no update operations for subscriptions, and some optional data sources degrade softly, but agents can accomplish the intended research, monitoring, and memory tasks without dead ends.

Resources