Skip to main content
Glama

dns

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?

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description's behavioral disclosures are consistent and richly detailed: account requirement, parallel decomposition into 5,798 tools, gap recovery hops, contradiction scans, fetchable citation_uri, semantic excerpting, and latency expectations (15-60s, up to ~90s for thorough). This far exceeds annotation coverage.

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 long but information-dense and every sentence earns its place: account requirement, routing, decomposition, output contents, gaps, depth tiers, citation fetchability, excerpting, latency. Front-loading is strong with the ACCOUNT REQUIRED warning and the ask_pipeworx alternative first. The only minor deduction is the parenthetical-heavy style that slows parsing.

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?

Given 2 parameters, no output schema, and heavy annotations, the description fully covers what an agent needs: prerequisites, timing, depth semantics, citation resolution, gap-handling, and comparison to siblings. Nothing essential is missing for correct invocation and expectation-setting.

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 description coverage is 100% and covers both parameters. The description adds useful behavioral nuance to depth ('standard adds a gap-recovery hop', 'thorough=6... full iterative hop'), but it doesn't add new semantic meaning beyond the schema's already-detailed enum descriptions. Baseline 3 plus the depth behavior context earns a 4.

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 uses specific verbs and resources: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources... in ONE call — this is NOT open-web search.' It clearly distinguishes deep_research from open-web search and from sibling ask_pipeworx. The wording names concrete source categories and the output packet, making the tool's 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 explicitly states when to use deep_research ('Best for broad/multi-part questions over structured data') and when not to ('For a single lookup use ask_pipeworx instead'). It also names ask_pipeworx as the fallback for unsigned users and differentiates from open-web search. This is strong routing guidance.

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

All dns 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

A3.8/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly described as currently identical, creating real ambiguity between two tools. The polymarket cluster (arbitrage, edges, fill_risk, edge_tracker, kalshi_spread, bet_research) has overlapping edge-finding purposes that rely on reading long descriptions to separate, and the DNS tools are so few that an agent cannot tell this is a 'dns' server at all.

Naming Consistency2/5

The set mixes at least four naming conventions: bare verbs (remember, forget, subscribe), verb_noun (dns_lookup, validate_claim, discover_tools), noun phrases (entity_profile, deep_research, recent_alerts), and brand-prefixed families (ask_pipeworx_*, polymarket_*, pipeworx_*). Each cluster is internally consistent, but the overall pattern is incoherent, including stray names like reverse_dns that invert the verb_first convention.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold for a heavy surface, and the count is wildly mismatched to the server's name: only 3 of 34 tools (dns_lookup, dns_lookup_all, reverse_dns) relate to DNS. The remaining 31 tools belong to unrelated domains (data research, prediction markets, memory, subscriptions, npm scanning), making the toolkit feel like a mislabeled grab-bag rather than a scoped server.

Completeness3/5

Judged against the server's stated 'dns' purpose, coverage is thin: read-only lookups only, with no WHOIS, DNSSEC, zone management, or write operations. Judged against the dominant inferred domain (a data-research/prediction-market platform), the surface is quite complete — query, grounded verification, deep research, profiles, comparisons, claim validation, discovery, subscriptions, alerts, and feedback all exist — though there is no tool to directly read a pipeworx:// citation URI.

Resources