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Glama

Askdata Xk

by io.github.pipeworx-io
OverviewAdmin

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?

Beyond the annotations (read-only, open-world, idempotent, non-destructive), the description adds highly important behavioral detail: the meaning and non-evidentiary nature of 'could_not_verify' (with an explicit warning not to treat it as evidence), the 'unsupported' outcome, the return of a verdict with citation, and the automatic routing to structured vs. grounded pipelines. It also warns about a specific failure mode (verification_error), which is exactly the kind of context an agent needs.

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 somewhat long, but it is well-structured: trigger phrases first, then purpose, usage, routing, return values, critical caveats, and finally the efficiency gain. Each sentence adds value, and the 'IMPORTANT for callers' section is necessary. It could be slightly more concise (e.g., trimming the seven trigger phrases), but it remains focused and front-loaded.

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?

There is no output schema, so the description carries the full burden of explaining what the tool returns. It lists all verdict types, the citation mechanism, the 'could_not_verify' error structure, and the 'unsupported' meaning. Combined with the rich annotations, this gives an agent everything needed to interpret results correctly, including edge cases. The description is exceptionally 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.

Parameters4/5

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

The schema already describes both parameters ('claim' and 'tolerance_pct') with 100% coverage, which sets a baseline of 3. The description adds meaningful context beyond the schema by mentioning 'exact percent-delta math', the default tolerance cap of 5, and the use of tolerance for hallucination detection (set 1–2). This enriches an agent's understanding of how to set the parameters.

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 opens with multiple natural-language trigger phrases ('fact check', 'verify the claim', 'confirm or refute') and immediately states the core function: 'natural-language claim verification against authoritative sources.' It clearly distinguishes this from sibling tools by specifying the two internal pathways (structured SEC EDGAR for company-financial claims, grounded pipeline for all else) and notes it replaces several sequential steps. This is a specific verb+resource+scope with strong differentiation.

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 a clear trigger condition. It also explains the two routing branches based on claim type. However, it does not explicitly name sibling tools as alternatives or state when NOT to use this tool (e.g., when the user just wants general information rather than a verdict), leaving some ambiguity against tools like ask_pipeworx_grounded or deep_research.

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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