Skip to main content
Glama

advice

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

A4.3/5.0
Behavior5/5

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

Beyond the safe-read annotations, it discloses account/paywall requirements, parallel decomposition, output packet fields, gaps[], contradictions[], fetchable citation_uri only when resolvable, semantic excerpting, and latency. These are exactly the behavioral traits an agent needs and they go far 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.

Conciseness1/5

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

The same multi-sentence paragraph is repeated many times in the description, with degrading copy, making it severely bloated. While the core content is useful, the structure fails the standard that every sentence should earn its place.

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 tool with no output schema, the description fully covers what the call returns, how gaps and contradictions are represented, how citations remain fetchable, how long the call takes, and the auth tiers. Nothing essential for invoking or interpreting the tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents both parameters at 100% coverage, including depth meanings, defaults, and paid status. The description mostly restates this and adds account/latency context, which is useful operationally but does not meaningfully extend parameter semantics 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 first paragraph is explicit: deep_research performs grounded multi-source research across Pipeworx's 1,517 structured data sources, decomposes a question into facets, and routes them to 5,798 tools in parallel. It also explicitly separates this from open-web search and from ask_pipeworx for single lookups, so the tool's identity is unambiguous.

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?

It gives concrete decision rules: use ask_pipeworx when not signed in or for a single lookup, and use deep_research for broad/multi-part questions over structured data. The examples ('compare X and Y's regulatory + financial exposure') further clarify the intended use case.

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

All advice 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

A3.7/5.0
Disambiguation2/5

The ask_pipeworx family is a major confusion source: ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly', and ask_pipeworx_grounded/deep_research heavily overlap with the base router. polymarket_edges vs polymarket_arbitrage and discover_tools vs suggest_questions also have fuzzy boundaries, though long descriptions partially mitigate the overlap.

Naming Consistency4/5

All tool names are lowercase snake_case, and most follow a verb_noun pattern (validate_claim, resolve_entity, compare_entities, generate_llms_txt). A few bare verbs (remember, recall, forget) and noun-style names (entity_profile, polymarket_arbitrage, pipeworx_trending) deviate slightly, but the overall style is predictable and readable.

Tool Count2/5

34 tools is heavy and spans several unrelated domains: Pipeworx data research, prediction markets, subscriptions, memory, AI visibility, advice slips, npm dependency checks, and llms.txt generation. The count is inflated by near-duplicate research routers and disconnected outliers like generate_llms_txt and scan_dependency, making the set feel like a kitchen sink rather than a focused server.

Completeness3/5

The Pipeworx research and prediction-market surfaces are quite complete (ask, grounded, deep research, entity profile, compare, resolve, validate, subscriptions with full lifecycle, memory with save/recall/delete). However, the server is named 'advice' yet the advice domain only has three thin tools (get/search/random) with no other operations, and the mixed domains leave obvious dead ends for any single stated purpose.

Resources