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Glama

Scryfall

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 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

A4.7/5.0
Behavior5/5

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

Beyond readOnly/idempotent annotations, the description discloses latency expectations (15-60s, thorough up to ~90s), result packet structure (verbatim evidence, confidence, source, fetched_at, citations), gap handling (never invented, explicit gaps[]), contradictions[] field, and the follow-up hop behavior. This is rich, precise behavioral detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very long and dense, with many parenthetical asides that make it harder to parse. However, the most critical info (account requirement, fallback tool, what it does) is front-loaded. Every sentence carries useful information, but the structure suffers from over-packing.

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 complex research tool with only 2 params, 100% schema coverage, and no output schema, the description provides everything needed: runtime, output shape, citation semantics, gap behavior, and the fallback route. An agent can decide to use the tool and interpret its response 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?

Schema coverage is 100%, so parameters are fully documented in the schema. The description adds value by explaining that depth maps to facet counts and hop behavior, and by clarifying that broad questions are welcomed. This goes beyond 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 opens with a clear verb-resource pair: grounded multi-source research across Pipeworx's structured data sources, and explicitly contrasts with open-web search. It names the sibling ask_pipeworx as the fallback and distinguishes this tool from single-lookup siblings like ask_pipeworx or bet_research.

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 says to use ask_pipeworx when not signed in, states depth:thorough requires a paid plan, and says broad/multi-part structured-data questions are the best fit versus a single lookup. It also names the decomposition behavior which is the core differentiator.

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

A3.9/5.0
Disambiguation3/5

Most tools have distinct purposes, but the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) plus deep_research creates real boundary confusion, and the six polymarket_* tools overlap enough to require careful reading. The few Scryfall card tools are clearly distinct from the Pipeworx bulk, but the name mismatch adds selection friction.

Naming Consistency4/5

All tool names use snake_case and most follow a verb_noun pattern (get_card, search_cards, resolve_entity, validate_claim). There are deviations like entity_profile, deep_research, and pipeworx_trending, but the nested families (ask_pipeworx*, polymarket_*) are internally consistent and predictable.

Tool Count2/5

35 tools is above the 25+ threshold and is especially mismatched with the server name 'Scryfall', which implies a focused MTG card server. Only 4 of 35 tools relate to Scryfall; the remaining 31 form a sprawling data-research platform that would be more appropriately split into separate servers.

Completeness4/5

The dominant Pipeworx data-research surface is remarkably complete: universal lookup, grounded answers, deep research, entity profiles, comparisons, entity resolution, claim validation, change feeds, subscriptions, memory, and discovery. The Scryfall subset covers core card lookup (search, get by name, random, list sets) but lacks rulings, set details, and card-by-ID lookups, which is a minor gap relative to the server's stated name.

Resources