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Glama

Space Feeds

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description layers on substantial additional behavior: account/paid-tier auth requirements, 15-90s latency, the full findings-packet structure, gaps[] with a 'never invented' guarantee, contradictions[] for standard/thorough, one-hop-at-a-time iteration semantics, and semantic excerpting of large records. Nothing here 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.

Conciseness3/5

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

Every sentence carries genuine guidance, but the description is a ~450-word single block for a two-parameter tool, and the depth paragraph near-verbatim repeats the schema's depth enum description. Scattered spelling noise (inconsistent renditions of tool names like ask_pipeworx and corrupted tokens) adds parsing cost without adding meaning. It would earn a 5 at half the length.

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?

No output schema exists, yet the description fully specifies the return packet, gaps[]/contradictions[] fields, latency, auth tiers, and even the post-call instruction to quote the pipeworx:// citation URI in user-facing answers. For a complex research tool with rich runtime behavior, nothing an agent needs to invoke it correctly or set expectations 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; the description adds real value beyond it with concrete example questions for `question` and the directive to enumerate all desired facets upfront. For `depth` it largely duplicates the schema's enum text, but contributes new context: thorough's plan-confirm-update flow, latency expectations per depth, and the paid requirement. Net-additive, though somewhat redundant.

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?

States a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources ... in ONE call', explicitly contrasted with 'NOT open-web search'. It names the output shape (findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation) and repeatedly distinguishes itself from siblings ('For a single lookup use ask_pipeworx instead'). The only downside is that the auth caveat precedes the purpose statement, but the purpose itself 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?

Provides explicit when-to-use ('Best for broad/multi-part questions over structured data') and multiple when-not-to-use rules with named alternatives: ask_pipeworx for unsigned-in users and single lookups, compare_entities for narrower structured comparisons, and recent_alerts/polymarket_* for time-sensitive market data. It also instructs the agent to state all facets upfront because the tool won't iterate on its own. This is exemplary routing guidance.

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

A3.6/5.0
Disambiguation2/5

Several tools occupy nearly the same niche: ask_pipeworx_beta is explicitly an identical duplicate of ask_pipeworx when no experiment is active, and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all overlap as question-answering entry points. Other families like entity_profile vs compare_entities vs recent_changes and ai_visibility_check vs scan_competitor_ai_presence also blur together despite long disambiguating descriptions.

Naming Consistency4/5

All tool names use a clean, readable snake_case style, and there are strong prefix families like ask_pipeworx, polymarket_, list_, and scan_. However, the set is not uniformly verb_noun: entity_profile, deep_research, recent_alerts, pipeworx_trending, and several others are noun phrases rather than actions, so the pattern is mostly consistent but not strict.

Tool Count2/5

34 tools is well beyond the 25+ threshold where a server starts feeling bloated, and the server name 'Space Feeds' suggests a narrow niche while most of the surface is a general data research, prediction-market, memory, and subscription platform. Each tool may be useful, but as a set the scope is sprawling rather than focused.

Completeness4/5

The major workflows have good lifecycle coverage: data lookup and grounded verification, entity resolution and profiling, prediction-market analysis, memory (remember/recall/forget), subscriptions (subscribe/list/unsubscribe/recent_alerts), and feed reading (list/read/fetch) are all represented. Minor gaps include a direct pipeworx:// citation reader and feed curation or management operations, but agents can work around those.

Resources