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Glama

Climatiq

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

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool read-only/open-world/idempotent, and the description adds substantial beyond-annotation behavior: the verdict taxonomy, the crucial distinction between could_not_verify (check did not happen) and unsupported (no source found), and the tolerance override semantics. This fully discloses behavior a caller must know.

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?

Though moderately long, every sentence earns its place: usage triggers, routing logic, verdict values, and two critical error-semantics clarifications. It is front-loaded with natural-language examples and avoids fluff.

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?

Despite no output schema, the description covers what the tool returns (verdict, actual value, citation, reasoning) and explains edge cases (could_not_verify vs. unsupported). Given the tool's complexity and the 100% schema coverage, this is a complete and self-sufficient description.

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?

Schema covers 100% of parameters, and the description enriches both: claim gets realistic examples, tolerance_pct gets why/when to override (hallucination detection) and default behavior (implied by wording, capped at 5). This goes beyond schema descriptions by adding intent and boundary conditions.

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 concrete natural-language triggers ('Is it true that…', 'fact check') and a specific verb+object: verify factual claims against sources. It differentiates this from the sibling Q&A tools (ask_pipeworx_grounded) by framing it as a verification tool with a verdict, not an open-ended answer tool.

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?

Explicitly states when to use ('Use whenever... factually correct') and describes routing for company-financial vs. all other claims. It does not name sibling alternatives or state when not to use it, but the trigger conditions and the 'Replaces 4–6 sequential calls' note give clear practical guidance.

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

A4/5.0
Disambiguation3/5

Most tools have distinct jobs and the descriptions are unusually detailed with cross-references, but there is real overlap in the ask_pipeworx family (ask_pipeworx_beta is explicitly identical to ask_pipeworx right now), the Polymarket edge/arbitrage cluster, and some company-research tools. An agent can usually pick correctly, but only after reading long descriptions carefully.

Naming Consistency3/5

All names are snake_case and many are clear verb_noun forms like estimate_emissions or list_subscriptions, but the set also contains descriptive noun phrases (entity_profile, recent_changes, ai_visibility_check), brand-prefixed names (pipeworx_feedback, polymarket_edges), and bare memory verbs (remember, recall, forget). This is a readable but mixed convention rather than one predictable pattern.

Tool Count2/5

34 tools is well above the comfortable ceiling, and the set bundles several unrelated domains: Climatiq emissions, Pipeworx data research, prediction-market analytics, memory, subscriptions, AI visibility, llms.txt generation, and npm dependency checks. It feels heavy and redundant, with ask_pipeworx_beta and the AI-visibility pair as candidates for removal or merging.

Completeness4/5

The core query workflows are well covered: emission factors lead into estimation, general lookups have plain/grounded/deep variants, claim validation and entity profiles exist, and subscriptions/memory have full lifecycles. The main gaps are minor—no batch emissions endpoint or direct order execution—so agents can work around them.

Resources