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Glama

Elexon

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

A4.6/5.0
Behavior5/5

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

It goes far beyond the annotations by explaining the internal pipeline, the meaning of each verdict, and the critical distinction between could_not_verify and unsupported. It also discloses how tolerance_pct affects results and warns callers not to treat could_not_verify as evidence, which is essential behavioral information.

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 densely informative, with clear structure: examples, use cases, pipeline details, output semantics, and caller warnings. Each sentence adds value, though it could be slightly tightened without losing context.

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 there is no output schema, the description fully explains return values (verdict enum, grounded value, citation, reasoning) and error handling. It also covers the two major claim categories and the fallback behavior, making it complete enough for an agent to use confidently.

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?

The schema already describes both parameters, but the description adds valuable semantics: tolerance_pct overrides the claim's implied tolerance, has a bounded range, and is recommended for hallucination detection. The claim parameter's natural-language examples in the schema are reinforced without adding much beyond that.

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 identifies the tool as a natural-language claim verification service with a specific verb and resource. It also distinguishes between the structured SEC/financial fast path and the grounded pipeline for all other factual claims, making it distinct from generic search or research tools.

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 tells the agent when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also gives granular guidance on financial vs. non-financial claims, but does not explicitly name alternative tools or state clear when-not-to-use conditions.

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
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx_grounded/deep_research heavily overlap as high-level routing entry points, and the five Polymarket tools (edges, arbitrage, edge_tracker, fill_risk, bet_research) cover closely related concerns. The descriptions are detailed, but an agent can easily select the wrong entry point.

Naming Consistency3/5

All names are snake_case and readable, but conventions are mixed: verb-first names (query_dataset, validate_claim, discover_tools) coexist with noun-phrase names (system_demand, entity_profile, recent_changes), and prefix families are applied inconsistently (elexon_* and polymarket_* exist, but bet_research, generation_by_fuel, and system_demand have no prefix). The pattern is understandable but not predictable enough to be considered consistent.

Tool Count2/5

36 tools is well above the 25-tool threshold for a heavy surface, and many tools are orthogonal to the nominal Elexon scope: memory (remember/recall/forget), subscriptions, npm dependency scanning, and llms.txt generation. The count forces significant discovery overhead and makes the set feel bloated rather than well-scoped.

Completeness4/5

The Elexon core is solid: elexon_list_datasets plus query_dataset covers all 84 BMRS datasets, with direct shortcuts for system prices, generation by fuel, and system demand. The broader Pipeworx side also covers research, entity resolution, prediction-market analysis, memory, and subscriptions without obvious dead ends, though a few minor gaps exist such as limited non-npm dependency scanning and no direct Elexon-specific tools for every dataset family.

Resources