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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

A4.6/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context: it decomposes questions into facets, routes to 5,798 tools in parallel, returns a findings packet with verbatim evidence + confidence + source + fetched_at + stable pipeworx:// citation, never invents gaps, and explains the second-hop iteration with gap recovery. It also discloses latency (15-60s, thorough up to ~90s) and the condition under which citation_uri appears. A small deduction because not all edge behaviors (e.g., what happens on rate limits) are disclosed, but this is rich beyond the 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 (roughly 220 words) but every sentence earns its place: it covers the account requirement, alternatives, the research mechanism, outputs, depth semantics, gaps, latency, and citation behavior. It is front-loaded with the most critical gate (account required/sign in) and the key differentiator (structured data, not open-web). It loses a point for density and length, which could be trimmed by moving some detail to the schema, but it remains structured and readable.

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 2-parameter tool with 100% schema coverage and no output schema, the description is exceptionally complete: it explains what the tool returns (findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation), how it handles gaps (gaps[] never invented), how depth levels iterate, when citations are fetchable, and how long the call takes. Given the tool's complexity, the description covers all the context an agent needs to decide when and how to call it. The only minor gap is no explicit mention of the exact output structure beyond prose, but for an agent the prose is sufficient.

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 the baseline is 3. The description adds meaningful semantics beyond the schema: it clarifies that depth:'standard' is the default (schema says standard=3, default; description confirms and explains the gap-recovery hop), and it explains that depth:'thorough' is paid and requires a paid plan, which is not in the schema. It also gives a behavioral sense of depth:'quick' as single-hop. The description could have added more detail on what each depth costs or how to phrase the question, but the schema already covers the parameters well.

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 ('research'), a resource ('Pipeworx's 1517 STRUCTURED data sources'), and the key differentiator ('this is NOT open-web search'). It clearly distinguishes itself from ask_pipeworx and other siblings, and gives concrete examples of when to use it ('compare X and Y's regulatory + financial exposure').

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 explicitly says when to use this tool vs alternatives: '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'. It also provides the account/plan gate and what depth levels do, giving clear selection criteria.

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
Disambiguation3/5

Most tools have clearly described distinct purposes, but several overlap: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all occupy neighboring query/discovery territory. The Polymarket and memory tool families, by contrast, are well differentiated.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes verb-first names (ask_pipeworx, compare_entities, resolve_entity, validate_claim) with noun-phrase names (entity_profile, bet_research, recent_alerts, polymarket_arbitrage) and brand prefixes (pipeworx_*, polymarket_*). There is no consistent verb_noun pattern across the toolkit.

Tool Count2/5

34 tools is well beyond the heavy range, and the count is especially inappropriate because the server is named Kegg but only find, get_entry, and list_database relate to KEGG bioinformatics. The remaining 31 tools span unrelated domains (generic data research, prediction markets, memory, subscriptions, AI visibility, npm scanning), making the scope feel like several products merged into one.

Completeness2/5

As a KEGG server, the surface is severely thin: three read-only tools with no pathway mapping, sequence search, or cross-reference utilities. The Pipeworx research and Polymarket betting subsystems are more complete, but their presence under a Kegg server makes the overall surface incoherent rather than complete.