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Glama

Rxnorm

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?

Despite readOnly/idempotent annotations, the description adds substantial behavioral context: internal pipeline routing, verdict taxonomy, and the critical nuance that 'could_not_verify' is not evidence for/against a claim. Warns about verification_error{stage,detail} and 'unsupported' semantics. No contradiction with 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?

Approximately 180 words, front-loaded with purpose and usage. Content-dense with useful bullets and emphasis. While longer than a two-sentence ideal, the complexity of the tool (two paths, multiple verdicts, edge-case warnings) 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?

No output schema exists, yet the description explains the return value (verdict, actual value, citation, reasoning) and clarifies ambiguous states ('could_not_verify' vs 'unsupported'). It also covers both claim-type handling and the efficiency advantage, fully equipping an agent to use the tool.

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?

Schema description coverage is 100%, so baseline is 3. The tool description adds trigger phrases and an example for 'claim', and mentions 'exact percent-delta math' and 'tolerance' for 'tolerance_pct', but these largely echo the schema descriptions rather than introducing new meaning.

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?

Clearly states verb+resource: 'natural-language claim verification against authoritative sources.' Lists multiple trigger phrases ('fact check', 'verify the claim that') and explicitly distinguishes it via the verdict-output and replacement of sequential calls. Strongly differentiated from sibling tools.

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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' Also differentiates company-financial vs other claims. However, it does not name alternative tools or state when NOT to use it (e.g., for open-ended research), so lateral guidance is incomplete.

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

Several tools blur together: ask_pipeworx_beta currently duplicates ask_pipeworx exactly, ask_pipeworx/ask_pipeworx_grounded/deep_research/validate_claim all route natural-language questions to data sources, and the prediction-market tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, bet_research) have fuzzy boundaries. The detailed descriptions help, but an agent can easily misselect among them.

Naming Consistency3/5

All names are lowercase snake_case and the rxnorm_, polymarket_, and pipeworx_ prefixes create readable groupings, but the set mixes imperative verb-object names (validate_claim, list_subscriptions), noun-phrase names (entity_profile, recent_alerts), and bare verbs (remember, forget). It is readable but not a predictable uniform naming convention.

Tool Count2/5

35 tools is in the too-many band for a coherent server, and the Rxnorm identity makes it worse: only 4 tools are actually RxNorm-specific while 31 are unrelated Pipeworx, prediction-market, memory, and utility tools. A focused RxNorm server would need a fraction of this surface.

Completeness3/5

The four rxnorm_* tools plus resolve_entity cover the main RxNorm lookup flow (search, properties, related, NDC), so the core is not broken. But the set lacks a reverse NDC-to-concept lookup and the 31 non-RxNorm tools do not complete any single coherent domain, leaving notable gaps relative to the server's stated purpose.

Resources