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Numbers

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

A4.6/5.0
Behavior5/5

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

Beyond the annotations (read-only, open-world, idempotent), the description adds critical behavioral context: the distinction between 'could_not_verify' (a pipeline failure that is NOT evidence) and 'unsupported' (no source covers it), the two routing paths, and the return format. This significantly enriches the agent's understanding of expected behavior. 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?

The description is dense and front-loaded with call patterns, but it is somewhat long and includes some repetition (e.g., explaining the grounded pipeline multiple times). It could be slightly more structured, but every sentence carries meaningful information for this complex tool, so it earns a high score.

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 routing paths, five verdict types, error cases), the description is remarkably complete. It explains return values (verdict, value, citation, reasoning), clarifies the meanings of the most ambiguous verdicts ('could_not_verify' and 'unsupported'), and states when the tool should be used. With no output schema provided, this description carries the full burden and does so well.

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% for both parameters (claim, tolerance_pct), so the baseline is 3. The description adds some context about percent-delta math and hallucination detection tolerance, but mostly reinforces schema details rather than introducing new parameter semantics. It does not compensate for any gaps, but none are needed.

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 a specific verb+resource: 'validate_claim' with natural-language claim verification, and provides clear examples of user intents ('Is it true that…', 'fact check'). It differentiates itself by explaining its claim-verification output (verdict) and the routing of financial vs non-financial claims, which sets it apart from the broader research tools in its sibling list.

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?

Explicit usage guidance is provided: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also specifies sub-routing for company-financial claims via SEC EDGAR and any other claim via a grounded pipeline, plus notes that it replaces 4–6 sequential calls. This is clear when-to-use direction.

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

A3.8/5.0
Disambiguation3/5

Most tools have detailed descriptions that clearly delineate their purposes, but ask_pipeworx_beta is currently functionally identical to ask_pipeworx, and deep_research overlaps with ask_pipeworx for multi-part questions. The six polymarket_* tools also share related territory, though their boundaries are well-documented.

Naming Consistency3/5

All names use snake_case, and domain prefixes like polymarket_, pipeworx_, and ask_ add predictability. However, the set mixes verb-led names (compare_entities, resolve_entity, search_within) with noun-led names (entity_profile, recent_changes, bet_research), so there is no single consistent verb_noun convention.

Tool Count2/5

35 tools is well above the 15-25 'heavy' band and feels like a kitchen sink: the server bundles a data-research platform, prediction-market analysis, memory, subscriptions, number conversions, and web-dev utilities into one surface. Many of these are unrelated to the server's 'Numbers' identity.

Completeness4/5

The main subdomains are thoroughly covered: data lookup (ask_pipeworx variants, deep_research, validate_claim), prediction-market analysis (arbitrage, edges, fill risk, kalshi spread), memory (remember/recall/forget), and subscriptions (subscribe/list/unsubscribe/recent_alerts). Minor gaps exist, such as no subscription update operation, but core lifecycle coverage is strong.

Resources