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Data Gov In

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?

Beyond the readOnly/idempotent annotations, the description discloses internal behavior: two-pipeline architecture, verbatim evidence, judgment step, verdict vocabulary, and the precise meaning of could_not_verify (verification_error) versus unsupported. This is rich behavioral context that annotations alone do not provide.

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 front-loaded with trigger phrases and usage, then flows into pipeline specifics, return values, and critical caveats. Every sentence adds operational value; it is detailed but not redundant, appropriately sized for a complex tool that replaces multiple steps.

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 thoroughly covers return values (verdicts, actual value, citation, reasoning) and error semantics (could_not_verify vs. unsupported). It also covers both pipeline paths and the tolerance parameter, making it complete for an AI agent to invoke correctly in a wide range of claim-checking scenarios.

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?

Although schema coverage is 100%, the description adds meaningful semantics: it explains how tolerance_pct interacts with the 'percent-delta math' and recommends 1–2 for hallucination detection, which goes beyond the schema's dry definition. It also clarifies the expected natural-language format for claim with examples.

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 natural-language claim verification against authoritative sources, with explicit trigger phrases and a specific verb-resource pairing ('validate claim'). It distinguishes itself from sibling tools by stating it replaces 4–6 sequential calls and by detailing two distinct verification pipelines (structured SEC EDGAR vs. grounded), making its scope unambiguous.

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?

It gives direct usage guidance ('Use whenever the agent needs to check whether something a user said is factually correct') and explains the routing logic for financial vs. other claims. It also provides important exclusion semantics for could_not_verify, telling callers not to treat it as evidence, which is practical when-to-use advice.

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 heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language data questions. The six Polymarket/prediction-market tools also blur together, and ai_visibility_check versus scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Snake_case is used consistently, and most tools follow a verb-first or domain-prefixed pattern (ask_pipeworx, compare_entities, subscribe, polymarket_*). Minor deviations like entity_profile, resource_data, and ai_visibility_check are noun-first, but nothing is chaotic or mixed-cased.

Tool Count2/5

33 tools is excessive for a server named 'Data Gov In' whose actual domain-specific surface is only resource_data and resource_meta. The rest are generic Pipeworx, prediction-market, memory, and utility tools that do not belong to the apparent India open-data scope.

Completeness1/5

For a data.gov.in server, the surface is severely incomplete: there is no way to search or list datasets/resources, only fetch metadata and data for a known resourceId. The overwhelming majority of tools serve unrelated domains, so an agent using this server for Indian government data will hit dead ends immediately.

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