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Glama

Oeis

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

A5/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds significant context beyond annotations: the two pipeline routes (SEC EDGAR vs grounded), the exact verdict set, the distinction between 'could_not_verify' (pipeline failure, not evidence) and 'unsupported' (no source coverage), and the presence of verification_error. No contradictions 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence contributes: trigger phrases, purpose, routing logic, return format, critical caller-facing caveats, and efficiency note. It is front-loaded with the most important use-case first and uses caps for the 'IMPORTANT' warning, making it scannable despite 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?

Given the tool's complexity (two pipelines, six distinct verdicts, error semantics) and the absence of an output schema, the description covers all essential aspects: return values, citation format, error handling, and the crucial semantic of 'could_not_verify' vs 'unsupported'. It leaves little ambiguity for an agent.

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?

Schema already provides 100% coverage for both parameters, including examples for 'claim'. The description adds extra semantic value by explaining the tolerance_pct override behavior, default cap of 5, and recommendation for hallucination detection (1–2%). This goes beyond the schema's plain definition.

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 opens with concrete natural-language trigger phrases and explicitly states the tool verifies factual claims against authoritative sources, returning a verdict. It clearly distinguishes itself from general Q&A (ask_pipeworx) and research (deep_research) by focusing on claim verification and even notes it replaces a multi-step pipeline.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It further differentiates the fast path for company-financial claims from the grounded pipeline for other factual claims, and even explains efficiency benefits over sequential calls. This gives clear when-to-use guidance without naming sibling alternatives explicitly.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap, especially among ask_pipeworx variants (ask_pipeworx, ask_pipeworx_grounded, ask_pipeworx_beta) and prediction market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research). While descriptions help distinguish them, the boundaries are somewhat fuzzy.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use verb_noun (search_sequence, compare_entities), others use noun_verb (entity_profile, recent_changes), and some have prefixes (pipeworx_feedback, pipeworx_trending) while others do not (remember, forget). There is no uniform pattern, which reduces predictability.

Tool Count2/5

The server name 'Oeis' suggests a focus on integer sequences, but only 2 of 33 tools are OEIS-related. The remaining tools cover a vast array of unrelated domains (data retrieval, prediction markets, memory, subscriptions). This mismatch between name and scope makes the tool count feel excessive and poorly scoped.

Completeness4/5

Within the broad domains covered, the tool surface is quite complete. For memory, there are remember/recall/forget; for subscriptions, subscribe/unsubscribe/list_subscriptions/recent_alerts; for data retrieval, multiple entry points. Minor gaps exist, such as lack of OEIS sequence contribution tools, but overall the set is well-rounded.

Resources