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Glama

Temperature Random

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. Added

TDQS

A5/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior, so the bar is lower. The description adds crucial behavioral details: the exact meaning of 'could_not_verify' (check did not happen, with verification_error) and 'unsupported' (no source covered), and warns callers not to treat 'could_not_verify' as evidence. This is valuable context beyond the annotations.

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, then provides routing logic, output summary, and critical caveats. Every sentence earns its place — no redundant filler. Despite being longer than average, the complexity of the tool justifies the length, and the structure is logical and scannable.

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 adequately explains the return values (verdict types, actual value with citation, reasoning). It also covers edge cases ('could_not_verify', 'unsupported') and the dual-path routing. The description is self-contained for an agent to understand what the tool does, when to use it, and what to expect.

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?

Schema coverage is 100%, but the description enriches parameter semantics significantly. For 'tolerance_pct', it explains it overrides the claim-wording default, gives a use case (hallucination detection with 1–2), and states the default cap of 5. The 'claim' parameter is illustrated with concrete examples. This goes beyond the schema's basic descriptions.

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 uses specific trigger phrases like 'fact check' and 'verify the claim that' and distinguishes the resource (claims) and action (verify). It also differentiates from general Q&A tools by focusing on verification with a verdict-based output.

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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between two paths (company-financial via SEC EDGAR, other via grounded pipeline) and notes that it replaces 4–6 sequential calls, which is strong guidance on when to invoke this tool instead of chaining others.

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

A3.6/5.0
Disambiguation2/5

There are several overlapping clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions through the same 5,743-tool catalog, and ask_pipeworx_beta is currently an exact duplicate of ask_pipeworx. The Polymarket opportunity tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) also have fuzzy boundaries despite detailed descriptions.

Naming Consistency2/5

There is no consistent naming pattern across the set: some tools are verb-first (ask_pipeworx, compare_entities, validate_claim), some are noun-first (entity_profile, recent_changes, polymarket_edges), and some are compound/multi-word oddities (temperature_random_generate, ai_visibility_check). Small internal clusters like remember/recall/forget and subscribe/unsubscribe/list_subscriptions show mini-consistency, but the overall convention is mixed and unpredictable.

Tool Count2/5

With 32 tools, the server is over-stuffed, and many of them serve the same broad Pipeworx data/research purpose while one unrelated temperature tool rides along. The count is above the 25-tool threshold where a set starts to feel unwieldy, and several tools could be merged or dropped without losing real capability.

Completeness4/5

Assuming the intended scope is the Pipeworx data/agent platform implied by 31 of the 32 tools, coverage is strong: lookups, grounded verification, deep research, entity profiles, comparisons, entity resolution, claim validation, tool discovery, memory, subscription lifecycle, prediction-market analysis, execution risk, and feedback are all represented. The temperature_random_generate tool is a domain misfit rather than a completeness gap, and there are few obvious missing operations for the stated workflows.

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