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Glama

Usajobs

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 already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, lowering the bar. The description adds substantial behavioral detail: how financial vs. non-financial claims are routed, what each verdict means, and critically warns that 'could_not_verify means the check did not happen... must not be shown as one.' It also clarifies 'unsupported means we looked and cover no source for it.' This goes well beyond annotation-level disclosure.

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 longer than average but every sentence earns its place: trigger phrases, routing rules, verdict list, critical caveats, and efficiency note. It is front-loaded with natural-language examples that make the tool instantly recognizable, and contains no filler or redundancy.

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?

Given the tool's complexity, the description is thorough: it covers the full set of returned verdicts, the two execution paths, the meaning of error cases, and the return value format (actual value with pipeworx:// citation). No output schema exists, so this description carries the full burden—and it succeeds.

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

Parameters5/5

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

Schema coverage is 100% with both parameters well described. The description adds extra semantics, especially for tolerance_pct: 'Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection' and 'Default: implied by wording, capped at 5.' It also provides a concrete example for the claim param. This is meaningful added value beyond the schema.

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's function: 'natural-language claim verification against authoritative sources.' It uses a specific verb ('verify') and resource ('factual claims'), and distinguishes it from sibling tools by noting it 'Replaces 4–6 sequential calls' and providing example triggers. The two distinct paths (SEC EDGAR for financial claims, grounded pipeline for others) add specificity.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' which is a strong when-to-use. It also explains scope (company-financial vs. any other factual claim). However, it does not explicitly name alternative sibling tools like search or ask_pipeworx_grounded, nor state when not to use this tool—only implies it replaces a multi-step pipeline.

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

Multiple tools overlap significantly: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle factual queries, and the Polymarket suite (polymarket_arbitrage, polymarket_edges, bet_research) has fuzzy boundaries. Even with long descriptions, agents will struggle to choose correctly among these clusters.

Naming Consistency3/5

Naming is snake_case but inconsistent in style: some tools are verb-led (get_job, list_agencies, validate_claim), others are noun-led (polymarket_edges, ai_visibility_check, recent_changes). The pattern is predictable only in that everything is snake_case, but the verb/noun order varies.

Tool Count2/5

36 tools is excessive for a server nominally called 'Usajobs'; the bulk of tools (Pipeworx, Polymarket, memory) are unrelated to the server's apparent purpose. The count feels like a bundled platform rather than a focused tool set.

Completeness4/5

For the USAJOBS subset, coverage is solid: search, get_job, and reference lists for agencies/occupational series/pay grades cover the read-only domain. However, the broader tool surface is sprawling and lacks a clear organizational principle, making completeness hard to assess; major data lookup features are present but via meta-tools rather than dedicated ones.

Resources