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Glama

Opensea

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

A4.4/5.0
Behavior5/5

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

The description substantially exceeds the annotations by explaining error semantics: 'could_not_verify means the check did not happen... and must not be shown as one,' and 'unsupported means we looked and cover no source for it.' It also discloses the return structure (verdict, actual value, citation, reasoning) and internal routing, which are important behavioral details not visible in annotations alone.

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 information-dense and front-loaded with example queries. It is long but every sentence contributes meaning, including the 'IMPORTANT for callers' callout. It could be slightly tightened, but the length is justified by the tool's complexity.

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?

There is no output schema, so the description correctly takes on the burden of explaining return values: it lists verdict types, mentions the actual value with a pipeworx:// citation, and provides reasoning. It also explains routing and special verdict meanings, making it complete for a complex 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 coverage is 100% and both parameters ('claim' and 'tolerance_pct') already have detailed descriptions in the schema. The main description does not add additional parameter-level meaning beyond what the schema provides, so the baseline of 3 applies.

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 states a specific verb ('verify') and resource ('natural-language claim') with clear scope: 'natural-language claim verification against authoritative sources.' It distinguishes itself from siblings by focusing on fact-checking verdicts and the structured vs. grounded pipeline, and by noting it replaces 4–6 sequential calls.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims and any other factual claim. However, it does not mention alternative tools by name or state explicit when-not-to-use scenarios, 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

Tools cluster into overlapping groups: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates distinguished only by mode; bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. The five OpenSea read tools are distinct, but they are buried among several unrelated domains, making misselection likely.

Naming Consistency4/5

Names are overwhelmingly snake_case with a verb_noun structure (get_collection, list_owned_nfts, validate_claim, create nothing but still remember/unsubscribe). Pipelined families like ask_pipeworx_* and polymarket_* are consistent, with only minor deviations such as pipworx_trending or bet_research not following a clear verb-object pattern.

Tool Count2/5

36 tools is too many for a coherent server, and the count is inflated by at least four unrelated domains: OpenSea NFT reads, Pipeworx data lookup/research, Polymarket betting, and memory/subscription utilities. Only five tools actually relate to the server's stated OpenSea purpose, so the surface is heavily bloated with off-scope functionality.

Completeness2/5

The OpenSea-relevant tools cover basic read operations—collections, stats, single NFT, collection NFTs, and owned NFTs—but omit search, events, offers/listings, order book data, and account/contract details. The many unrelated Pipeworx tools do not fill these gaps, so an agent needing real marketplace behavior would hit dead ends.

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