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Glama

Stat Lv

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?

Annotations declare read-only/idempotent, but the description adds critical behavior beyond annotations: the distinction between 'could_not_verify' (a failure with verification_error, not evidence) and 'unsupported' (checked, no source). This is essential operational context not captured 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.

Conciseness4/5

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

The description is a single dense block but logically ordered: trigger phrases, use case, routing, returns, caveats. Each clause adds functional detail with no filler, though slightly long for a quick scan.

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?

For a complex tool with no output schema, the description exhaustively explains verdict values, citation format, reasoning, and the critical error semantics. It covers both claim types and the fallback routing, giving the agent enough to invoke it correctly.

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% and both parameters are fully documented in the schema. The tool description only alludes to tolerance behavior in the financial path and does not add new parameter guidance; baseline 3 is appropriate.

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 natural-language trigger phrases and explicitly states 'natural-language claim verification against authoritative sources.' It clearly distinguishes from sibling tools by focusing on verifying truth/falsity, not just retrieving info.

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?

It explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and goes further by distinguishing company-financial claims (SEC EDGAR) from other claims (grounded pipeline). It doesn't name alternative tools to avoid, but its scope definition is strong.

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
Disambiguation3/5

Several tools form overlapping families (ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research, plus the six polymarket_* tools), and ask_pipeworx_beta is currently an exact behavioral duplicate. The long descriptions usually disambiguate them, but an agent could still struggle to quickly choose between similar research and edge-detection tools.

Naming Consistency3/5

Names are uniformly snake_case and prefix families like pipeworx_* and polymarket_* help, but there is no consistent verb_noun pattern: subjects, table_meta, recent_alerts, and entity_profile are noun phrases while remember, generate_llms_txt, and compare_entities are action-first. The mixed conventions are readable but less predictable than a uniform pattern.

Tool Count2/5

34 tools is well into the 'too many' range for a single tool set, even if each is individually documented. Several could plausibly be consolidated, such as ask_pipeworx_beta, the polymarket edge tools, and the AI-visibility pair.

Completeness4/5

For its broad stated purpose, the set covers the full lifecycle: lookup/research, entity profiles, comparisons, claim validation, prediction-market edge analysis, memory, subscriptions, and discovery. Minor gaps exist, such as no direct fetch-by-URI tool or subscription update path, but most workflows have a clear route.

Resources