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Glama

Energi Data Dk

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

A5/5.0
Behavior5/5

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

Adds critical behavioral context beyond annotations: explains the two distinct verification paths, the exact verdict list, citation behavior, and the semantic distinction between could_not_verify and unsupported. No contradiction with readOnly/openWorld/idempotent 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?

Well-structured, front-loaded with purpose, uses examples inline, and every sentence adds value (trigger phrases, dual-path logic, caller warning, replaced-call count). Length is justified by complexity.

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?

No output schema exists, but description enumerates all possible verdicts, explains error handling, and covers both financial and non-financial claim routes, making it complete for an agent to choose and invoke safely.

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 already documents both params, but description adds meaning by explaining tolerance_pct overrides claim-implied tolerance, provides a use case (1–2 for hallucination detection), and notes default cap of 5.

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 trigger phrases and explicitly states it performs natural-language claim verification against authoritative sources, clearly distinguishing from sibling tools by its verdict-based output and dual-path handling (SEC EDGAR for company-financials, grounded pipeline for others).

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 states 'Use whenever the agent needs to check whether something a user said is factually correct' and provides exclusions by explaining could_not_verify vs unsupported, plus notes it replaces 4–6 sequential calls, giving clear when-to-use 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

A3.9/5.0
Disambiguation2/5

Several tools form overlapping clusters that are hard for an agent to distinguish: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all cover factual/research lookups, and the five Polymarket tools also overlap heavily. ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, making misselection essentially guaranteed in that pair. The descriptions are detailed, but the boundaries between many tools remain unclear at the set level.

Naming Consistency4/5

Almost all tool names are snake_case and mostly follow readable verb_noun or domain-specific patterns, such as ask_pipeworx*, resolve_entity, validate_claim, and polymarket_*. Minor deviations exist — bare verbs like remember/recall/forget/subscribe and noun-style names like spot_prices/co2_intensity — but there is no mixed casing and the overall pattern is predictable.

Tool Count2/5

34 tools is already over the 25+ threshold for a well-scoped server, and the mismatch is much worse because only three tools (co2_intensity, spot_prices, query_dataset) relate to the advertised Energi Data DK domain. The other 31 tools appear to belong to an unrelated general-purpose Pipeworx platform, so the count is not appropriate for the server's stated purpose.

Completeness3/5

For the stated Energi Data DK domain, energy data access is partially covered: two dedicated tools plus query_dataset as a generic escape hatch for all ~100 datasets prevents hard dead ends, but there are no typed tools for most of those datasets and no energy-specific monitoring/alerting. If the real intended domain is the broader Pipeworx platform, coverage is much stronger, but then the server name is misleading.

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