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Send That Email

by io.github.pipeworx-io

Deep Research

deep_research
Read-onlyIdempotent

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1498 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,733 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a hop field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).
questionYesThe research question, in natural language. Broad/multi-part is fine — decomposition is the point.

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses account and pricing requirements, the gaps[] vs 'never invented' behavior, hop fields and resolvable citation_uri, semantic excerpting instead of head-truncation, contradictions[] output, and latency ranges. This substantially exceeds what annotations alone convey, and nothing contradicts them.

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 most critical gating information (account required) is front-loaded, followed by capability, alternatives, and depth semantics. It is dense and thorough rather than bloated, though there is minor redundancy around gaps[] and citation_uri appearing twice. Every sentence carries operational weight.

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?

For a tool with no output schema, the description fully compensates: it explains findings packet fields, gaps[], contradictions[], timing, authentication, pricing tiers, depth behavior, and alternative routing. An agent has everything needed to select and invoke this tool correctly.

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?

Even though schema coverage is 100%, the description enriches both parameters meaningfully: question is clarified as natural-language and broad/multi-part, and depth is expanded from a facet count into paid-plan requirements, gap-recovery hops, lead-chasing, and contradiction scans. This is genuine added value beyond the schema's enum descriptions.

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 highly specific verb and resource: 'Grounded multi-source research across Pipeworx's 1497 STRUCTURED data sources' that decomposes questions and returns a findings packet. It explicitly distinguishes itself from open-web search and from sibling ask_pipeworx, leaving no ambiguity about what it does.

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?

Provides explicit when-to-use and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data', 'For a single lookup use ask_pipeworx', and for breaking news 'prefer ask_pipeworx'. It also gives an account-gated fallback ('If you are not signed in, use ask_pipeworx instead').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Send That Email toolsInspect
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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