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Glama

Cms Open Payments

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. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond those annotations: it explains the could_not_verify vs. unsupported distinction, warns that could_not_verify is not evidence, and describes the return structure (verdict, actual value, citation, reasoning). This is significant additional transparency without contradicting 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 fairly long but well-structured: it opens with natural-language examples, states the purpose, outlines two processing paths, lists return values, highlights an important error-handling caveat, and concludes with the efficiency benefit. Every sentence carries meaningful information, though it could be slightly tightened without losing substance.

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 there is no output schema, the description fully covers what the tool returns (verdicts, actual value, citation, reasoning) and explains error semantics (could_not_verify vs. unsupported). It also describes the underlying pipelines (SEC EDGAR/XBRL vs. grounded) and the scope of claims, providing a complete picture for correct invocation.

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?

While the input schema already describes both parameters with 100% coverage, the description enhances understanding of tolerance_pct by explaining its default behavior ('implied by wording, capped at 5'), its override function, and a concrete use case (set 1–2 for hallucination detection). This adds clear operational guidance 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 purpose: natural-language claim verification against authoritative sources. It is distinguished from siblings by focusing on fact-checking with a verdict output and mentions replacing multiple sequential calls, making its role unmistakable.

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,' providing clear invocation context. It also details two distinct processing paths (company-financial vs. any other claim), but it does not explicitly exclude or compare to alternatives like ask_pipeworx_grounded, so it lacks explicit when-not-to-use 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

A3.6/5.0
Disambiguation3/5

Tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very similar, causing potential confusion. The open_payments_* tools are clearly differentiated, but the mix of generic Pipeworx tools with domain-specific ones creates overlapping purposes.

Naming Consistency2/5

Tool names mix conventions: some use snake_case (ask_pipeworx, open_payments_company), while others use less consistent patterns (deep_research, generate_llms_txt). The open_payments_* tools have a consistent prefix, but the overall set lacks a unified naming scheme.

Tool Count2/5

With 41 tools, the server is heavily overloaded for a domain focused on CMS Open Payments. The majority of tools are general-purpose Pipeworx tools unrelated to the server's name, making the count feel excessive and unfocused.

Completeness3/5

The open_payments_* tools cover the CMS Open Payments domain well (search, company, physician, history, etc.). However, the inclusion of many unrelated Pipeworx tools means the server as a whole is not cohesive, and the completeness of the named domain is overshadowed.

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