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Glama

Speedrun

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?

Even though annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, the description adds substantial behavior beyond them: the tool requires an account (free, with a paid tier for depth:thorough), returns explicit gaps[] instead of inventing answers, uses parallel decomposition, cites fetchable pipeworx:// URIs only when served, excerpts large records semantically, and reports expected latency (15-60s, up to ~90s for thorough). These are exactly the non-obvious behavioral traits an agent needs.

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 front-loaded: account requirement and routing guidance appear first, followed by mechanism, output shape, caveats, and latency. It is long, but every sentence carries a distinct behavioral fact or routing cue; the semicolon-delimited depth semantics are efficient. It could be tightened slightly by trimming parenthetical examples, but the length is justified by the tool's complexity.

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 2 well-schema'd params, no output schema, and a rich set of annotations, the description covers everything an agent needs: prerequisites/account gating, exact input expectations, output packet shape, honesty guarantees via gaps[], citation resolvability, hop semantics, and latency. The only omission is an explicit statement about the lack of pagination or how many findings per facet, but that is a minor gap for a research tool whose output is a findings packet.

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 both parameters are already documented in the schema. The description still adds value by unpacking the depth enum in operational terms (quick=3 single hop; standard=3 adds gap recovery + contradictions[]; thorough=6 paid, iterative hop) and by clarifying that 'question' should be broad/multi-part and 'decomposition is the point.' This goes beyond the schema's wording, though slightly redundant with it.

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 precise verb+resource: multi-source research across 1506 structured data sources, and explicitly contrasts with open-web search. It names the concrete deliverable (findings packet with evidence, confidence, source, fetched_at, citation, gaps[]) and distinguishes itself from ask_pipeworx and ask_pipeworx_beta siblings by scope and use case. The 'NOT open-web search' warning and mention of single-call broad research make its purpose unmistakable.

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 gives explicit when-to-use ('Best for broad/multi-part questions over structured data') and when-not-to-use ('For a single lookup use ask_pipeworx instead', 'BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'). It also provides a concrete example question to disambiguate from siblings, and lists both named alternatives. This fully routes an agent to the right tool.

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

Many tools have distinct purposes, but there is overlap in data querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, etc.) and prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). Detailed descriptions help differentiate them, but the number of similar-sounding tools increases the chance of misselection.

Naming Consistency3/5

Tool names show mixed conventions: some use verb_noun (e.g., find_user, list_subscriptions), others are noun_verb (e.g., entity_profile, bet_research), and there are prefixes like polymarket_ and pipeworx_. While subgroups are internally consistent, the overall set lacks a unified pattern.

Tool Count2/5

With 34 tools spanning speedrun.com queries, Pipeworx data access, memory management, subscriptions, and prediction markets, the server bundles multiple domains. The scope is too broad for a coherent single server; splitting into separate servers would improve usability.

Completeness4/5

Within each domain (speedrun.com, Pipeworx, Polymarket), the tool set covers key operations comprehensively, including research, arbitrage, fill risk, and monitoring. Minor gaps exist (e.g., no tool to place bets), but the overall surface is well-covered for the advertised functionalities.

Resources