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Glama

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 1517 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 5,798 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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."New value: +"How 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)."
  2. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus a contradictions[] scan across findings)."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."
  3. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). \"thorough\" also runs a second ITERATIVE hop — a planner inspects the first-pass findings/gaps and chases the most valuable leads or recovers gaps, resolving multi-step questions in one call."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus a contradictions[] scan across findings)."
  4. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans)."New value: +"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). \"thorough\" also runs a second ITERATIVE hop — a planner inspects the first-pass findings/gaps and chases the most valuable leads or recovers gaps, resolving multi-step questions in one call."
  5. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true. The description adds significant behavioral context: it states what happens if not signed in, that it never invents gaps[], that citations are resolvable only when the source emits one, that large records are semantically excerpted, that contradictions[] are returned for standard/thorough, and that latency is expected (15-60s, up to ~90s for thorough). It also discloses parallel tool routing (5,798 tools) and the gap-recovery/lead-chasing iterations. 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 long but information-dense; nearly every sentence adds a distinct fact (sign-in requirement, alternative tool, source scope, mechanism, output contents, gap handling, citation resolvability, latency). It is front-loaded with the most operationally critical information (account requirement and fallback). It loses one point for being somewhat sprawling and parenthetical-heavy, but it earns its length given the tool's complexity.

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 research tool with a rich input schema and no output schema, the description is remarkably complete: it covers failure mode (gaps[]), citation behavior (always fetchable, with caveat), latency, depth variants, contradictions, excerpting, and alternatives. An agent can decide whether to call this tool, how to phrase the question, which depth to choose, and what to expect in the response. The lack of an output schema is compensated by the detailed findings-packet description.

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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds meaningful semantics beyond the schema: it explains how depth values map to facets/hops ('quick=3 (single hop)', 'standard=...gap-recovery hop...', 'thorough=6...iterative hop'), and clarifies the meaning of 'broad/multi-part is fine' for the question parameter. It doesn't merely repeat schema text.

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 verb ('researches'), a clear resource ('Pipeworx's 1517 STRUCTURED data sources'), and explicitly distinguishes itself from open-web search. It also names the sibling alternative (ask_pipeworx) for signed-out users. The scope is precise: broad/multi-part questions over structured data, with a one-call decomposition-and-parallel-routing behavior.

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?

The description gives explicit when-to-use guidance ('Best for broad/multi-part questions over structured data'), explicit when-not-to-use/sign-in guidance ('If you are not signed in, use ask_pipeworx instead'), and names the alternative tool directly. It also contrasts with open-web search. Depth levels are explained in the schema and reinforced in the description with timing and behavioral differences.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same 5,529 tools; polymarket_arbitrage and polymarket_edges both find tradeable opportunities; discover_tools and suggest_questions both serve discovery. The beta tool being an exact duplicate makes misselection highly likely.

Naming Consistency3/5

Most action tools follow verb_noun (ask_pipeworx, compare_entities, discover_tools, list_groups, resolve_entity, search_datasets, suggest_questions, validate_claim), but there is significant mixing with noun_noun (dataset_details, entity_profile, organization_details, pipeworx_feedback, polymarket_arbitrage) and adjective_noun (deep_research, recent_alerts). The Polymarket family is consistently prefixed, but overall the server mixes several conventions.

Tool Count2/5

36 tools far exceeds the typically well-scoped range, and the server bundles what appear to be five separate concerns: Italian open data, Pipeworx universal query, entity/report utilities, prediction-market analytics, and meta/memory/subscription features. Many tools could be consolidated (e.g., ai_visibility_check and scan_competitor_ai_presence; discover_tools and suggest_questions), making the set feel bloated.

Completeness4/5

The broad domain of structured data research and prediction-market edge is largely covered: universal routing, grounded answers, deep research, entity resolution, profiles, comparisons, change feeds, claim verification, arbitrage scans, fill-risk, subscriptions, memory, and feedback. Minor gaps exist—no direct tool to fetch raw CKAN resource URLs, no exhaustive list of all 5,529 tools, and no actual order execution on prediction markets—but these are workable around.