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Glama

Treasury Fiscal

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. Added

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already indicate readOnly, openWorld, and idempotent hints. The description adds substantial behavioral context: the SEC/XBRL fast path vs grounded pipeline, exact percent-delta math, verdict meanings, and the critical distinction between could_not_verify and unsupported. This goes well beyond what annotations provide.

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 dense and information-rich, with no redundancy. The opening quoted examples take space but clarify usage. It is occasionally verbose, but for a tool with no output schema, the length is justified and well-structured with clear sections.

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 return values: verdict types, actual value with citation, reasoning, and error states. It also covers edge cases (could_not_verify vs unsupported) and notes the efficiency gain over sequential calls, making the tool fully understandable in context.

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 100% coverage with detailed descriptions for both parameters, including tolerance_pct semantics and default behavior. The tool description itself adds no additional parameter-level detail beyond the schema, so the baseline of 3 is appropriate.

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 that the tool verifies natural-language claims against authoritative sources, with explicit example phrasings like 'fact check' and 'verify the claim that...'. It distinguishes itself from sibling tools by focusing on claim verification with verdict output.

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 explicit trigger phrases and states 'Use whenever the agent needs to check whether something a user said is factually correct.' It also distinguishes between company-financial and other factual claims with routing details. However, it does not name alternative tools or give explicit when-not-to-use guidance, 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

A3.6/5.0
Disambiguation2/5

Most of the surface is dominated by overlapping meta-tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) plus paired helpers (discover_tools/suggest_questions, ai_visibility_check/scan_competitor_ai_presence), so an agent can easily pick the wrong one. The seven treasury_* tools are clearly distinct, but they are a small island in a much larger ambiguous set.

Naming Consistency3/5

Names are broadly snake_case and family-prefixed (treasury_*, polymarket_*, pipeworx_*), which helps, but the pattern is not consistently verb_noun: ask_pipeworx, bet_research, entity_profile, deep_research, search_within, and recent_changes mix verb, noun, and product-specific naming styles. Within families it is readable, but across the whole set it is inconsistent.

Tool Count2/5

37 tools is too many for a server whose label is 'Treasury Fiscal'; only about six tools are treasury-specific and the rest are unrelated research, betting, memory, and subscription utilities. This is well into the 25+ heavy range and would make tool selection expensive and error-prone.

Completeness2/5

For a Treasury/Fiscal server the surface is thin: debt, receipts, customs duty, average rates, exchange rates, and net cost cover only a slice of Treasury data. Missing obvious components such as daily yield curves, auction calendars/results, federal outlays/spending by agency or function, and tax or appropriations data; the broad ask_pipeworx router softens but does not fill these as first-class tools.

Resources