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Glama

Xero

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

A4.8/5.0
Behavior5/5

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

Annotations provide readOnly/openWorld/idempotent hints, but the description adds high-value context: the exact verdict set, that could_not_verify means the check did not happen and must not be treated as evidence, that unsupported means no source exists, and the fast-path vs grounded pipeline behavior. This goes well beyond structured fields.

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 longer than average but front-loaded with natural-language triggers and examples. Every sentence contributes—routing rules, return values, error semantics, efficiency benefit—with no filler. It could be trimmed slightly, but the density justifies the 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 compensates by enumerating all possible verdicts, explaining error semantics, mentioning the citation format, and clarifying source routing. This fully equips an agent to invoke the tool and interpret results, even in edge cases.

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 covers 100% of parameters, so baseline is 3. The description adds meaningful guidance: concrete example strings for the claim parameter, and for tolerance_pct it explains overriding the implied tolerance, suggests 1–2 for hallucination detection, and notes the default cap of 5. This enriches the schema's dry parameter descriptions.

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 states a specific verb and resource: 'natural-language claim verification against authoritative sources.' It distinguishes from sibling tools by explicitly explaining it replaces 4–6 sequential calls and by splitting financial vs non-financial verification paths, making its niche clear.

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?

It gives an explicit trigger: 'Use whenever the agent needs to check whether something a user said is factually correct.' It then provides a branching rule for company-financial claims vs all others, and clarifies the critical distinction between could_not_verify and unsupported, which prevents misuse.

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
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap (e.g., ai_visibility_check and scan_competitor_ai_presence are related; memory tools remember/recall/forget form a clear subgroup). Descriptions are detailed enough to differentiate, but the broad scope may cause occasional mis-selection.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (list_accounts, get_profit_and_loss), others are single words (forget, recall), and some use snake_case with mixed verbs (ai_visibility_check, ask_pipeworx, bet_research). No uniform pattern makes the set harder to navigate.

Tool Count3/5

25 tools is high but not extreme given the broad scope (accounting, betting, data queries, npm, memory, etc.). However, the server tries to cover too many domains, making it feel bloated. Each tool is individually useful, but the count is borderline excessive for coherence.

Completeness2/5

The Xero accounting subset is incomplete: only list and get operations, no create/update/delete for invoices, contacts, or accounts. Other domains (betting, npm) are covered well, but the core accounting purpose has significant gaps that will hinder agents.

Resources