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Epa Emissions

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 1500 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 5,743 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

A4.8/5.0
Behavior5/5

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

Even with readOnly/idempotent annotations, the description adds significant behavioral context: account/pricing prerequisite, no invented findings (gaps[]), second-hop gap recovery, contradictions[], fetchable citation_uri, semantic excerpting, and expected latency. Nothing in the description conflicts with the 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 long, but the tool is semantically rich and the most important constraints (account requirement, not-open-web, alternatives) are front-loaded. A few details could be tightened, but each sentence carries functional information.

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?

There is no output schema, so the description carries the burden of explaining the return shape, and it does: findings packet fields, gaps, contradictions, hop, citation_uri. It also covers prerequisites, alternatives, depth semantics, and latency — nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds value by tying depth values to behavior (standard gap recovery vs thorough lead-chasing), the paid-plan requirement for 'thorough', and latency expectations. It does not restate the schema; it enriches the enum semantics.

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 precise action ('Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources... in ONE call'), names the output (findings packet with evidence/confidence/source/citation), and explicitly distinguishes itself from open-web search. It also gives concrete example questions, making the resource scope unmistakable.

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 provides explicit routing rules: 'If you are not signed in, use ask_pipeworx instead', 'Best for broad/multi-part questions over structured data', and 'For BREAKING or colloquial CURRENT-NEWS ... prefer ask_pipeworx'. This is exactly the when/when-not/alternative guidance an agent needs.

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

B3.3/5.0
Disambiguation3/5

The tool set contains multiple groups with overlapping purposes (e.g., ask_pipeworx/ask_pipeworx_grounded/deep_research for data queries, multiple Polymarket tools, and memory tools). However, detailed descriptions help differentiate them, so ambiguity is moderate but not severe.

Naming Consistency2/5

Naming conventions are inconsistent: some tools follow verb_noun (ask_pipeworx, compare_entities), others use domain-prefixed noun_verb (ghg_emissions_by_sector, polymarket_arbitrage). This mix, combined with a server name that doesn't match the tool domain, makes the naming pattern unclear.

Tool Count1/5

With 35 tools, the server is overloaded, especially given its name 'Epa Emissions' which suggests a narrow focus. Only 5 tools (ghg_*, tri_*) are related to emissions; the rest are unrelated, making the count inappropriate for the server's assumed purpose.

Completeness2/5

For an EPA/emissions server, the tool set is incomplete: it lacks other emissions data (e.g., air quality, water quality, enforcement). The inclusion of many unrelated tools (e.g., Polymarket, memory) does not compensate for missing core emissions coverage.

Resources