Skip to main content
Glama

Federal Register

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?

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description explains crucial behavior: the two possible processing paths, the exact verdict values, and the critical distinction between 'could_not_verify' (system failure) and 'unsupported' (no source found). This prevents misinterpretation of results, which is highly valuable.

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 long but every sentence carries essential information: trigger examples, use cases, path differentiation, return values, edge-case semantics, and efficiency benefits. It is well-structured and front-loaded with the most important purpose at the start.

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 no output schema exists, the description fully compensates by explaining return values (verdict types, actual value, citation, reasoning) and clarifying ambiguous cases. It also covers the tool's role in replacing multiple sequential calls, making it complete for a complex, high-stakes verification tool.

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?

Although the schema already documents both parameters with examples, the description adds meaningful usage guidance, especially for tolerance_pct: it explains how it overrides implied tolerance, recommends values for hallucination detection (1–2), and states the default cap of 5. This goes beyond the schema's basic type/range description.

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 verification service, with specific trigger phrases and a clear verb-object structure ('verify the claim that…'). It distinguishes itself from sibling tools by detailing the structured SEC EDGAR/XBRL path and the grounded pipeline, showing it is the dedicated fact-checking tool.

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?

Provides an explicit usage condition ('Use whenever the agent needs to check whether something a user said is factually correct') and differentiates between company-financial claims and other factual claims. It does not name specific alternative tools to avoid, but the context is clear enough for most cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Federal Register toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.5/5.0
Disambiguation1/5

Several tools are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, and ask_pipeworx_grounded overlaps heavily with them. Additionally, entity_profile, compare_entities, deep_research, and validate_claim all cover similar company/factual research territory, creating frequent selection ambiguity.

Naming Consistency2/5

Naming mixes multiple conventions: descriptive lowercase phrases (ai_visibility_check, compare_entities, valid claim) coexist with verb_noun (search_documents, recent_rules) and inconsistent underscores (ask_pipeworx vs ask_pipeworx_grounded, resolve_entity). There is no single recognizable pattern.

Tool Count1/5

The server is named 'Federal Register' but only 3 of 34 tools (search_documents, recent_rules, get_document) relate to that domain. The other 31 tools form a sprawling Pipeworx data and prediction-market suite, making the count extreme and inappropriate for the declared purpose.

Completeness2/5

For the stated Federal Register domain, the surface is minimal: search, recent listing, and single-document retrieval, with no docket browsing, full-text search within documents, or agency-specific navigation. The broader Pipeworx capability set is comprehensive but irrelevant to the server's name, leaving obvious gaps for the actual purpose.

Resources