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Glama

Pangaea

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 already cover readOnly, openWorld, idempotent, and non-destructive, and the description adds substantial behavioral context: the dual fast-path/grounded-pipeline routing, the full verdict set, the critical distinction between could_not_verify (not evidence) and unsupported (no source), and the inclusion of pipeworx:// citations and reasoning. This goes well beyond the annotations, so 5 is appropriate.

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 a single dense paragraph but every sentence earns its place: trigger phrases, usage guidance, routing logic, return value, and important caller caveats. It is front-loaded with the most actionable info and avoids fluff, making it efficient despite its length.

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?

With no output schema, the description fully explains the return shape (verdict, value with citation, reasoning), covers all verdict meanings and error cases, and gives clear invocation context. The tool's complexity (two routing paths, multiple verdicts, open-world semantics) is matched by complete coverage, so 5 is warranted.

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 coverage is 100%, so both params are already documented well. The description adds meaningful extra semantics for tolerance_pct: it overrides the tolerance implied by the claim wording and recommends 1–2 for hallucination detection, plus notes the default cap of 5. This is beyond the schema, so it deserves a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a set of natural-language triggers ("Is it true that…" / "fact check" / "verify the claim that…") and defines the resource as "natural-language claim verification against authoritative sources." The verb+resource is specific and clear, but it does not explicitly differentiate from sibling tools like ask_pipeworx_grounded or search, so it stops short of a 5.

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 states "Use whenever the agent needs to check whether something a user said is factually correct," which is explicit guidance on when to use. It also explains the internal routing for company-financial vs. other claims. However, it provides no explicit when-not-to-use or named alternatives, so it earns a 4 rather than a 5.

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

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and validate_claim both perform claim verification, and polymarket_edges and polymarket_arbitrage both surface trading opportunities. While descriptions are detailed, the boundaries are fuzzy and agents could easily select the wrong tool.

Naming Consistency3/5

Naming mixes verb-first (search, subscribe, recall, validate_claim) with noun-first (dataset, facets, recent, entity_profile) conventions, and some names are just adjectives or nouns. Consistent prefixed groups exist (polymarket_*, ask_pipeworx_*), but the overall style is inconsistent and the server name 'Pangaea' doesn't align with the dominant 'pipeworx' prefix.

Tool Count2/5

36 tools is excessive for a coherent set, especially given the server bundles unrelated domains (earth science, general data research, prediction markets, memory, utilities). Several tools are redundant (e.g., ask_pipeworx_beta duplicates ask_pipeworx), and many are niche (ai_visibility_check, generate_llms_txt, scan_dependency) that don't fit the apparent primary purpose.

Completeness3/5

The PANGAEA dataset surface covers search, retrieval by ID/DOI, recent, and facets, but lacks export or citation tools. The Pipeworx research tools are broad, but prediction market access has no simple market-price query (only analysis-oriented tools), and there are notable gaps in lifecycle coverage for some subdomains.

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