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Glama

Mychem

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 5,801 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

A5/5.0
Behavior5/5

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

Annotations already mark it readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavior beyond that: account requirements, paid tier for 'thorough', expected latency (15-90s), never-invented gaps[], fetchable citations, semantic excerpting, hop fields, and contradictions[]. No statement contradicts 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.

Conciseness5/5

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

Long but every sentence earns its place; the most important operational constraints (account needed, paid depth, alternative tool) are front-loaded. Dense semicolon-separated details avoid fluff while covering mechanisms, outputs, failure modes, and performance. Formatting with bolded key terms aids scanning.

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?

Despite no output schema, the description fully characterizes the return value (findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[], hop field) and the failure behavior (empty gaps[], long duration). For a complex research tool, this is complete enough for an agent to invoke and interpret results without guessing.

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

Parameters5/5

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

Schema coverage is 100% (both 'question' and 'depth' have descriptions), and the description adds meaning beyond the schema: it defines what each depth value does in terms of hops, gap recovery, contradiction scans, and paid access. The parameter descriptions plus prose give an agent everything needed to pick correct values.

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 names a specific verb and resource ('grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources in ONE call'), explains the mechanism (decomposition, parallel routing across 5,801 tools), and clearly distinguishes it from ask_pipeworx. An agent can immediately tell what this tool does and how it differs from siblings.

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 states when to use it ('best for broad/multi-part questions over structured data'), when not to ('For a single lookup use ask_pipeworx'), and when to prefer an alternative ('For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'). Also explains depth-level behaviors, so an agent can select the right mode.

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
Disambiguation2/5

The set contains several near-duplicate entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) plus a dense family of polymarket_* and chemical lookup tools whose boundaries are subtle. An agent can easily pick the wrong router variant, edge-scanning tool, or chemical search tool.

Naming Consistency3/5

Snake_case is used throughout, but naming style is mixed: some tools are verb-first (query, resolve_entity, validate_claim), some are noun/domain-first (entity_profile, recent_changes, polymarket_arbitrage), and the ask_pipeworx variants use suffixes instead of a consistent verb pattern. It is readable, but there is no predictable convention beyond snake_case.

Tool Count2/5

34 tools is well past the comfortable range, and the count is especially hard to justify because they span unrelated domains: chemistry lookups, a Pipeworx data gateway, prediction-market analytics, memory, subscriptions, AI-visibility checks, and npm dependency scanning. Many of these have no obvious connection to the 'Mychem' name or to each other.

Completeness4/5

Within each contained workflow the surface is fairly complete: chemistry has search/fetch/metadata, Pipeworx has lookup/research/discovery/validation, subscriptions and memory both have lifecycle coverage, and the Polymarket family covers research, edge detection, persistence, and fill risk. Some minor gaps exist (no general web retrieval or execution/trading action), but agents can work around them.

Resources