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Nasa Cmr

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?

Annotations already mark readOnly, openWorld, idempotent, and non-destructive, and the description adds substantial behavior: account and paid-tier requirements, expected latency, gaps[] and contradictions[] return fields, citation_uri resolvability, semantic excerpting, and gap-recovery hops. No contradiction with annotations is present.

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 genuinely dense; nearly every sentence adds operational value, from auth to routing to latency to output format. It could be slightly more scannable, as the core operation appears after the auth and alternative-tool caveats, but the structure is logical and front-loaded with critical constraints.

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?

With no output schema, the description carries the full burden of explaining the return contract, and it does so thoroughly: findings packet fields, confidence, source, fetched_at, hop, citation_uri, gaps[], contradictions[], and failure modes. It also covers auth, latency, depth semantics, and alternative tool routing, making it complete for an agent to invoke correctly.

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?

The schema covers both parameters at 100%, so the baseline is 3. The description adds useful extra meaning by explaining depth behavior ('standard re-angles unanswered gaps', 'thorough additionally chases leads' and is paid) and by clarifying that the question can be broad/multi-part.

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 specific operation: grounded multi-source research across 1500 structured data sources, decomposing the question into facets and routing them in parallel. It clearly distinguishes itself from siblings by saying 'this is NOT open-web search' and by contrasting with ask_pipeworx and its intended use cases.

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 routing guidance is provided: use ask_pipeworx when not signed in, for single lookups, or for breaking-news topics; use deep_research for broad/multi-part structured-data questions. This directly addresses when and when not to use the tool versus alternatives.

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

The tool set mixes NASA-specific tools with a large number of financial and prediction market tools, causing confusion. Within the non-NASA tools, there is significant overlap (e.g., multiple Polymarket analysis tools, several research tools with vague boundaries).

Naming Consistency3/5

Most tools use snake_case, but naming patterns are inconsistent: some start with verbs (search_collections, get_collection), others are noun phrases (entity_profile, deep_research). There is no uniform verb_noun convention across the set.

Tool Count2/5

With 33 tools, the count is high. Although it may be appropriate for the underlying domain (data/prediction markets), it is excessive for the server's stated NASA focus, where only 3 tools are relevant.

Completeness1/5

For a server named 'Nasa Cmr', the tool set is severely incomplete: only three tools cover NASA Earth science (search/granules/collections), lacking any support for missions, datasets, or advanced queries. The non-NASA tools are comprehensive but disconnected from the server name.

Resources