Skip to main content
Glama

art

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?

Beyond the annotations (readOnly, openWorld, idempotent), the description details parallel decomposition across 5,743 tools, gap recovery behavior, contradictions[] scanning, hop fields, fetchable citation_uri semantics, semantic excerpting of large records, account/pricing requirements, and latency expectations. This far exceeds what annotations convey.

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 every sentence carries distinct information. It is front-loaded with the critical account requirement and auth alternative before moving into scope and behavior. The density is justified for a complex tool, though minor reorganization could improve scannability.

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 compensates thoroughly by specifying the return packet: verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[], hop field, and citation_uri criteria. It also covers latency, auth, and result semantics, making the tool fully usable without additional lookups.

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% for both parameters, so baseline is 3. The description adds value by explaining depth options in behavioral terms (quick=3 facets, standard adds gap recovery + contradictions, thorough is paid and adds iterative lead-chasing) and emphasizes that question accepts broad/multi-part natural language. This enriches rather than duplicates the schema.

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 states the tool's purpose: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources' in ONE call. It explicitly distinguishes itself from open-web search and contrasts with ask_pipeworx, making its 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?

Usage guidance is explicit and actionable: 'If you are not signed in, use ask_pipeworx instead', 'For a single lookup use ask_pipeworx', and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. Depth-level recommendations are also provided, leaving no ambiguity about when to choose this tool.

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

All art toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.5/5.0
Disambiguation2/5

Tools are a mix of art-related and Pipeworx data tools, with no clear boundary. Many Pipeworx tools overlap in functionality (e.g., ask_pipeworx, deep_research, bet_research), causing confusion. Art tools are distinct but sparse.

Naming Consistency2/5

Tool names mostly use snake_case but follow inconsistent patterns: some are verb-based (ask_pipeworx, search_artworks), others are noun-based (entity_profile, pipeworx_feedback). There is no unified convention across the server.

Tool Count1/5

33 tools is excessive for an art-focused server; only 4 tools (search_artworks, get_artwork, get_departments, generate_llms_txt) relate to art. The rest are from a different domain (Pipeworx), creating a severe scope mismatch.

Completeness2/5

For the art domain, only basic search and retrieval are provided; missing browsing, filtering, or creation tools. The Pipeworx tools are comprehensive but irrelevant to the server's stated purpose, leaving the art surface incomplete.

Resources