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Glama

Comtrade

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

A4.8/5.0
Behavior5/5

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

Beyond the annotations, the description discloses account and paid-plan requirements, expected latency ranges, parallel decomposition/routing mechanics, the never-invented gaps[] behavior, contradictions[], hop and citation fields, and semantic excerpting of large records. These are material behavioral traits not present in the annotations. No contradiction with readOnlyHint, openWorldHint, idempotentHint, or destructiveHint exists.

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 dense and somewhat run-on, but each clause carries a distinct, decision-relevant fact. It is logically sequenced from prerequisites to core behavior to output details to latency, and its length is justified by the tool's complexity, though some sentences could be tightened.

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?

Even without an output schema, the description covers the full return packet (verbatim evidence, confidence, source, fetched_at, stable citations), gap behavior, contradictions, multi-hop resolution, and latency expectations. Combined with rich annotations and 100% parameter schema coverage, nothing needed for correct invocation is missing.

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, but the description adds meaningful parameter semantics: depth:'thorough' requires a paid plan, 'standard' and 'thorough' return contradictions[], quick is single-hop, and depth choices map to latency expectations. This adds practical guidance beyond the schema's enum descriptions.

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 action and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in one call, and explicitly distinguishes this from open-web search. It also names the sibling it is not, stating 'For a single lookup use ask_pipeworx', so an agent can clearly differentiate it.

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 provides explicit when-to-use guidance ('Best for broad/multi-part questions over structured data'), explicit when-not-to-use guidance ('For a single lookup use ask_pipeworx'), and a prerequisite fallback ('If you are not signed in, use ask_pipeworx instead'). It even gives example questions, leaving little room for mis-selection.

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

Some tools have near-identical purposes (ask_pipeworx vs ask_pipeworx_beta are currently identical; polymarket_arbitrage vs polymarket_edges both find opportunities), but detailed descriptions and distinct input patterns mostly help an agent choose. A few discovery/verification tools (suggest_questions vs discover_tools, validate_claim vs ask_pipeworx_grounded) also overlap, creating residual ambiguity.

Naming Consistency3/5

All names are snake_case and many use domain prefixes (comtrade_, polymarket_, pipeworx_), but the set mixes verb_noun (compare_entities, discover_tools), bare verbs (remember, forget, subscribe), and noun phrases (entity_profile, recent_changes). This inconsistency, plus the use of domain-specific prefixes as a substitute for a uniform verb_noun style, makes the naming pattern only moderately predictable.

Tool Count2/5

35 tools is well beyond the typical well-scoped range, and the vast majority (prediction markets, memory, subscriptions, feedback, npm dependency checks) have nothing to do with the server's apparent Comtrade trade-data purpose. This bloat makes the set feel unfocused, even though some meta-tools serve a general data-access mission.

Completeness4/5

For the Comtrade trade-data domain, the four comtrade_* tools cover country codes, top commodities, top partners, and detailed bilateral trade values — enough for most queries, with minor gaps like time-series trends or tariff lookups. For the broader Pipeworx data-access scope, the set is extensive (ask_pipeworx, deep_research, entity_profile, validate_claim, subscriptions), so no severe dead ends are apparent.

Resources