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Glama

Edmtrain

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

A4.4/5.0
Behavior5/5

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

The description provides extensive behavioral detail beyond the annotations. It explains the exact verdict values, the meaning of 'could_not_verify' (with a warning that it is not evidence), the distinction between 'unsupported' and failure states, and the return format including citations. It also discloses the two distinct processing paths. This far exceeds the annotations' readOnly/openWorld/idempotent hints and adds critical decision-making context.

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 long but information-dense, and the structure is logical: trigger phrases, usage, processing details, return values, and special caller warnings. It includes an 'IMPORTANT for callers' section that highlights critical edge cases. While a few phrases could be tightened (e.g., the long list of non-financial claim types), every sentence contributes to understanding. Slightly verbose but well-organized.

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?

The description is remarkably complete for a tool with no output schema. It explains the input (claim), the parameter options, the return verdicts, the evidence format, the citation mechanism, and the failure modes (could_not_verify, unsupported). It also contextualizes the tool within the larger workflow by noting it replaces multiple sequential calls. No significant gaps are apparent.

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?

The input schema already provides 100% coverage for both parameters, with clear descriptions for 'claim' and 'tolerance_pct'. The tool description adds some context about the tolerance behavior (default capped at 5, overrides implied wording) but does not significantly enrich the parameter semantics beyond what the schema already states. The baseline of 3 is appropriate given the high schema coverage.

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. It provides specific trigger phrases ('fact check', 'verify the claim'), distinguishes the tool from siblings by focusing on factual validation, and even explains the internal routing logic (SEC EDGAR vs grounded pipeline). This goes far beyond a tautology and provides a rich, specific verb+resource definition.

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 states when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies that non-financial claims automatically fall through to the grounded pipeline, and notes that it replaces 4–6 sequential calls. However, it does not explicitly name sibling tools as alternatives or provide when-not-to-use scenarios, so it stops short of a perfect 5.

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

A3.9/5.0
Disambiguation2/5

Several tools have overlapping or near-identical purposes: ask_pipeworx and ask_pipeworx_beta are currently functionally identical, ask_pipeworx_grounded is the same router with an extraction step, and discover_tools vs suggest_questions both serve as 'what can I do here' entry points. The polymarket_* family is more distinct, but the ask_pipeworx/deep_research overlap alone makes tool selection genuinely ambiguous.

Naming Consistency4/5

The vast majority of tools follow lowercase snake_case with recognizable domain prefixes (ask_pipeworx*, polymarket_*, pipeworx_*), which is a decent pattern. However, there are bare-noun tools (events, locations, recall, forget) and mixed verb-first vs noun-first ordering (list_subscriptions vs entity_profile, scan_dependency vs polymarket_edges), so it is not perfectly uniform.

Tool Count2/5

33 tools is well above the comfortable range and feels heavy even for a broad data-research platform. Many tools are narrow variations (five polymarket analysis tools, four ask_pipeworx variants, three memory tools) that could plausibly be consolidated or exposed as parameterized modes rather than separate top-level tools.

Completeness4/5

For the actual domain suggested by the tool names and descriptions—structured data research, entity profiling, claim verification, and prediction-market analysis—the surface is quite complete: lookup, grounded answers, deep research, comparisons, recent-change tracking, subscriptions, memory, and arbitrage/fill-risk analysis are all covered. However, relative to the server name 'Edmtrain', the event-discovery surface is extremely thin (only events and locations), which is a notable mismatch.

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