Skip to main content
Glama

Json

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 readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds significant behavioral context beyond these: it defines the verdict taxonomy (confirmed, approximately_correct, refuted, inconclusive, unsupported, could_not_verify), explains the verification_error structure, and warns that could_not_verify must not be treated as evidence. This enriches the safety and failure-mode understanding beyond what annotations alone provide.

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 thorough and well-structured but somewhat long. It front-loads trigger phrases, then explains routing, return values, and special caller warnings. Each sentence adds value, though the 'IMPORTANT for callers' clause could arguably be merged with the verdict definitions to reduce redundancy. Overall, it's efficient for a tool with this complexity, so a 4 is appropriate.

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 no output schema, the description fully explains return values: a verdict, the actual value with a pipeworx:// citation, and reasoning. It also explains error handling (could_not_verify with verification_error) and differentiates unsupported. It covers both financial and non-financial routing and mentions tolerance semantics. This is complete for a verification tool with these inputs and annotations.

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?

Schema coverage is 100%, with both claim and tolerance_pct fully documented in the schema. The description adds examples but does not introduce new parameter semantics beyond what the schema already states (e.g., tolerance_pct range and override behavior). Per the rubric, baseline 3 is appropriate when schema does the heavy lifting, and the description doesn't compensate with extra details.

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's purpose: natural-language claim verification against authoritative sources. It lists trigger phrases like "Is it true that…" and "fact check," and explains the two paths (SEC EDGAR/XBRL for company-financial claims, grounded pipeline for others). This distinguishes it from sibling tools like compare_entities or ask_pipeworx_grounded by explicitly stating what it verifies and how.

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?

The description explicitly states when to use: "Use whenever the agent needs to check whether something a user said is factually correct." It also clarifies the meaning of special verdicts (could_not_verify vs unsupported) and notes that it replaces 4–6 sequential calls, giving efficiency context. No exclusions are needed because it covers both financial and non-financial claims through routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Json toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, from querying data (ask_pipeworx) to entity profiling (entity_profile) to prediction market analysis (polymarket_arbitrage, polymarket_edges). Even similar tools like deep_research and ask_pipeworx are differentiated by scope (single vs. multi-facet). There is no ambiguity.

Naming Consistency4/5

The vast majority of tools use snake_case (e.g., validate_claim, compare_entities). However, a few tools are single words (forget, recall, remember, subscribe, unsubscribe) which breaks the pattern slightly. This is a minor inconsistency.

Tool Count4/5

With 33 tools, the set is on the larger side but well-justified by the broad scope of the server (data querying, entity research, prediction markets, JSON utilities, subscriptions, etc.). Each tool serves a specific need, making the count appropriate.

Completeness5/5

The tool surface covers virtually all expected operations for the domain: querying, profiling, comparison, change tracking, validation, discovery, subscriptions, memory, and utilities. No obvious gaps are present for the intended use case of accessing Pipeworx data and related tasks.

Resources