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Glama

Uuid

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 declare readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavioral context: the dual-path routing, the meaning of could_not_verify (failure with verification_error, not evidence) and unsupported (no source coverage), and the return of a verdict with citation. This goes well beyond the annotations.

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 well-organized: trigger examples, usage statement, path details, return format, and a caller caution. Each sentence contributes necessary information for a complex verification tool, with no filler; however, it is more verbose than needed for a simpler tool.

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 compensates by enumerating the possible verdicts (confirmed, approximately_correct, etc.), explaining the actual value and citation, and detailing failure semantics. It also covers the two execution paths, giving an agent a complete picture of expected outcomes.

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 full descriptions for both parameters (claim and tolerance_pct) at 100% coverage. The description mentions 'exact percent-delta math' and tolerance behavior but does not materially enhance parameter understanding beyond the schema, so the baseline of 3 applies.

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 states the tool performs 'natural-language claim verification against authoritative sources' with concrete trigger phrases and examples. It differentiates from siblings by focusing on fact-checking and explicitly notes it replaces 4–6 sequential calls, leaving no ambiguity about its purpose.

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?

It provides an explicit 'Use whenever the agent needs to check whether something a user said is factually correct' and describes the two processing paths (company-financial vs. other factual claims). However, it does not explicitly name a sibling alternative or state when not to use the tool, but the replacement note and fallback behavior give strong 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

A3.8/5.0
Disambiguation3/5

Most tools have distinct purposes, but several overlap heavily: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, ai_visibility_check is a single-entity version of scan_competitor_ai_presence, and multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk) share fuzzy boundaries. The lengthy descriptions help, but an agent could easily select the wrong tool.

Naming Consistency4/5

The overwhelming majority follow a clear verb_noun or noun_phrase pattern (ask_pipeworx, resolve_entity, validate_claim, generate_uuid, discover_tools). The ask_pipeworx_beta/ask_pipeworx_grounded variants and brand-name tools like pipeworx_trending are minor deviations, but the overall convention is remarkably consistent across 33 tools.

Tool Count2/5

33 tools is heavy for a single MCP server, especially one named 'Uuid' where only 2 tools (generate_uuid, validate_uuid) relate to the apparent name. The remaining 31 tools constitute a sprawling data-research platform that would normally be its own server. The count stretches beyond what is typically coherent.

Completeness3/5

As a data-research platform, the surface is quite complete: lookup, grounded answers, deep research, entity resolution, comparisons, claim validation, subscriptions, memory, and feedback are all covered. However, for the nominal 'Uuid' domain, only generation and validation exist — missing anything like UUID namespace generation, timestamp extraction from v1/v7, or bulk operations — creating a glaring mismatch between server name and actual tool scope.

Resources