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Glama

Pvgis

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

A4.5/5.0
Behavior5/5

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

The description goes well beyond the read-only/idempotent annotations by disclosing the two-pipeline routing (SEC vs grounded), the exact verdict vocabulary, and the crucial semantics of 'could_not_verify' (i.e., the check did not happen and must not be treated as evidence) and 'unsupported' (no source exists). It also mentions the return of verbatim evidence with citations and the fact that it replaces multiple sequential calls.

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 densely packed with useful information—query examples, routing logic, output structure, and important caller warnings—yet every sentence earns its place. It is organized into clear sections and avoids redundancy or 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?

Even without an output schema, the description fully covers the return values (verdict types, actual value with citation, reasoning), explains error semantics, and covers both financial and non-financial claim paths. An agent has all necessary context to invoke the tool and interpret results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides thorough descriptions for both parameters: 'claim' with a concrete example and 'tolerance_pct' with its overrides, default, and range (0.5–50). With 100% schema coverage, the description adds little new meaning to the parameters themselves; it only indirectly references percent-delta math in the financial path. This matches the baseline of 3.

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 verifier with explicit example queries ('fact check', 'verify the claim that…'). It distinguishes between company-financial claims routed to SEC EDGAR+XBRL and all other factual claims routed to a grounded pipeline, making its scope unambiguous and distinct from sibling research/search 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 states an explicit when-to-use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies that every factual claim automatically falls into one of two paths, implying coverage of all verification needs. However, it does not explicitly name alternatives or provide 'when-not-to-use' conditions, so it falls short of 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

B3.4/5.0
Disambiguation3/5

Multiple tools serve overlapping query purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with subtle differences that are hard to distinguish without careful reading. Similarly, prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges) have overlapping scopes.

Naming Consistency2/5

Names are highly inconsistent: verb_noun (ask_pipeworx, resolve_entity), noun_descriptive (entity_profile, polymarket_arbitrage), and simple nouns (tmy, pvgis). No clear pattern emerges, making it hard to anticipate tool names.

Tool Count3/5

34 tools is borderline high for a single server. While some utility tools (remember, forget) are justified, many tools are very narrowly scoped (tmy, generate_llms_txt) and could be merged or omitted.

Completeness3/5

The server covers a wide breadth (data querying, prediction markets, solar energy, AI visibility) but feels like a collection of unrelated domains. Core operations for data querying are present, but the solar tools (monthly_radiation, pv_performance) seem orphaned from the rest.

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