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Mast Nasa

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 1506 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 5,767 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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint/idempotentHint, but the description adds substantial behavioral context: parallel decomposition, findings packet contents, explicit gaps[] instead of invented answers, contradictions[] for deeper depths, semantic excerpting, latency expectations, and account/tier requirements. 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 information-rich, front-loading the critical account/alternative guidance before explaining the tool. It is longer than strictly necessary, but nearly every sentence adds operational value for an agent deciding whether and how to call it.

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 there is no output schema, the description does an excellent job explaining what the agent will receive: verbatim evidence, confidence, source, fetched_at, stable citations, gaps, and contradictions. It also covers latency, authentication, tier limitations, and fallback behavior, leaving no critical gap.

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%, with both question and depth parameters already well-documented including enum meanings. The description adds some usage flavor like 'broad/multi-part is fine' and the paid-plan constraint for 'thorough', but does not need to compensate for schema gaps.

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?

Description states a specific verb and resource: 'grounded multi-source research across Pipeworx's 1503 STRUCTURED data sources... in ONE call'. It clearly distinguishes itself from open-web search and from ask_pipeworx, making its purpose unambiguous relative to siblings.

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?

Explicitly tells an agent when to use another tool: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx'. It also states when deep_research is the right choice: 'Best for broad/multi-part questions over structured data'.

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
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap among ask_pipeworx, ask_pipeworx_grounded, and deep_research, all of which query the Pipeworx database with different levels of structure. The detailed descriptions help differentiate them, but the overlap is notable.

Naming Consistency4/5

All tool names use snake_case consistently, which is good. However, the naming conventions vary: some are descriptive phrases (e.g., ai_visibility_check), others are verb_noun (e.g., list_subscriptions), and some are compound nouns (e.g., entity_profile). Lack of a single pattern reduces consistency slightly.

Tool Count3/5

34 tools is on the higher side for a single server, but it may be justified given the broad scope of Pipeworx data sources. However, the server name 'Mast Nasa' implies a focus on astronomy, yet only a few tools relate to that domain, making the count feel inflated and unfocused.

Completeness3/5

The tool set is comprehensive for the Pipeworx data platform, covering querying, grounding, entity resolution, comparison, subscriptions, and more. However, for the implied NASA/Mast domain, the surface is severely incomplete with only four astronomy-specific tools, leaving obvious gaps.

Resources