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Glama

Attom

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

A4.5/5.0
Behavior5/5

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

Beyond annotations, the description adds crucial behavioral context: the dual pipeline (SEC EDGAR fast path vs. grounded pipeline), the full verdict set, the requirement not to treat could_not_verify as evidence, and the meaning of unsupported. This is essential for a caller to correctly interpret results and goes far beyond what annotations provide.

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 dense but well-structured: trigger phrases, use case, pipeline distinction, output shape, error semantics, and efficiency benefit each earn their place. It is front-loaded with the purpose and maintains conciseness while covering all essential aspects.

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 a complex tool with no output schema, the description fully covers the return structure (verdicts, citation, reasoning) and clarifies the critical failure modes (could_not_verify and unsupported). It also explains the two processing paths, giving the agent sufficient information to invoke the tool and interpret results correctly.

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 the schema already fully explains both parameters (claim and tolerance_pct). The description adds domain context about financial vs. other claims, which indirectly relates to claim processing, but it does not enrich the parameter syntax or semantics beyond what the schema already provides.

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 factual claim'), explicitly defining the tool as 'claim verification against authoritative sources.' It also distinguishes from siblings by emphasizing the verdict-based output and the scope (checking truth of user statements), setting it apart from generic research or Q&A 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?

The description gives clear when-to-use guidance ('Use whenever the agent needs to check whether something a user said is factually correct') and clarifies the interpretation of special verdicts like could_not_verify and unsupported. However, it does not name specific alternative tools or state explicit when-not-to-use conditions, so it falls short of full exclusion 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

A3.7/5.0
Disambiguation2/5

Several tools are nearly indistinguishable without deep reading: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same universal query, and the beta currently behaves identically. The prediction-market clister (polymarket_arbitrage, pollymarket_edges, pollymarket_edge_tracker, pollymarket_fill_risk, pollymarket_kalshi_spread) and value-estimation tools (attom_avm, attom_assessment, attom_rental_avm) have fuzzy boundaries that will cause misselection.

Naming Consistency4/5

The vast majority of tools follow a consistent lower_snake-case convention with clear prefixes (attom_*, polymarket_*, pipeworx_*) and verb-noun forms (generate_llms_txt, list_subscriptions, resolve_entity). Minor deviations exist like the bare memory verbs remember, recall, forget and domain-noun names entity_profile, bet_research, but the overall pattern is predictable and readable.

Tool Count2/5

39 tools is well above the comfortable 3-15 range and signals scope creep: the server bundles real-estate, prediction markets, company research, memory, subscriptions, web utilities, and a universal data router. Many of these could be grouped into a smaller number of composite tools, as the descriptions themselves already suggest (e.g. ask_pipeworx as the default entry point).

Completeness3/5

Coverage is deep for prediction markets, company financials, and real-estate, with complete memory and subscription lifecycles (remember/recall/forgeet, subscribe/list/unsubscribe/recent_alerts). However, many advertised domains (weather, clinical trials, news, government records) are only reachable through the generic ask_pipeworx router rather than dedicated tools, and scan_dependency is npm-only, leaving obvious gaps for other ecosystems.

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