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Send That Email

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.

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the readOnlyHint/idempotent annotations by explaining critical behavioral nuances: the meaning of 'could_not_verify' vs. 'unsupported' (check didn't happen vs. no source exists), the presence of verification_error{stage,detail}, and the warning not to show could_not_verify as evidence. It also describes the routing to SEC EDGAR vs. grounded pipeline, giving the agent important expectations about how results are produced.

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 longer than average but efficiently front-loads user-facing trigger phrases and provides essential behavioral context. Every sentence contributes value: purpose, routing, return types, error semantics, and rationale. Slightly verbose in the middle but not padded; earns its 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?

Despite no output schema, the description fully enumerates possible verdicts, explains the difference between could_not_verify and unsupported, and describes the return payload (value, citation, reasoning). It also covers routing logic and error handling, making the tool's behavior predictable for an agent in varied scenarios.

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 the schema already describes both parameters well (claim with examples, tolerance_pct with range and purpose). The description does not add parameter-level detail beyond what's in the schema, so it meets the baseline for high coverage without needing to compensate.

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 identifies the tool's purpose: verifying natural-language factual claims against authoritative sources. It includes multiple trigger phrases ('fact check', 'verify the claim that...') and distinguishes itself from general Q&A or research tools by focusing on claim verification and returning a verdict. This is a specific verb+resource with clear scope.

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?

The description explicitly states when to use this tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides routing rules for company-financial vs. other factual claims, and notes that the tool 'Replaces 4–6 sequential calls,' reinforcing why it's a better alternative than composing multiple steps. No named alternatives, but the when-to-use is explicit and practical.

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

A3.5/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer queries with subtly different guarantees. The Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, polymarket_edge_tracker) also has fuzzy boundaries. Despite detailed descriptions, an agent could easily pick the wrong tool.

Naming Consistency2/5

Naming conventions are inconsistent: there are verb_noun names (discover_tools, resolve_entity), noun-based names (polymarket_edges, pipeworx_trending), single verbs (remember, recall, forget), and odd constructions like send_that_email_analyze. No clear pattern dominates, making it hard to predict tool names.

Tool Count2/5

32 tools is heavy for a focused server, and most tools are unrelated to the server's apparent email-sending purpose. The count feels bloated and the scope mismatched, though it is not extreme enough for a 1.

Completeness1/5

Given the server name 'Send That Email', the tool surface is severely incomplete: there is only an email analysis tool and no actual sending, drafting, or mailbox management. The bulk of the tools address data lookup and research, leaving the core email workflow entirely unimplemented.

Resources