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Glama

Peeringdb

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

A4.5/5.0
Behavior5/5

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

Annotations already mark this read-only/idempotent, and the description adds substantial context: account and paid-tier auth needs, output structure (findings packet with evidence, confidence, source, fetched_at, citations), explicit gaps[] and no-invention guarantees, contradictions scanning, semantic excerpting, and latency expectations. This far exceeds what annotations alone provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

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

The description is extremely bloated: the same core paragraph — 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources... For a single lookup use ask_pipeworx instead' — is repeated many times in a single run-on block. This severely hurts scanability and violates the principle that every sentence should earn its place.

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 the absence of an output schema, the description compensates by explaining the findings packet, citation_uri fetchability, gaps[], contradictions[], multi-hop depth behavior, and latency. An agent has enough behavioral detail to invoke the tool correctly without needing external documentation.

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 description does not need to document parameters from scratch; it still adds value by giving example question shapes ('compare X and Y's regulatory + financial exposure') and clarifying that broad/multi-part natural language is appropriate. It also flags the paid plan restriction for depth:thorough, which supplements the enum schema.

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 clearly identifies a specific verb+resource: 'grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in ONE call, with decomposition and parallel routing. It also distinguishes itself from open-web search and from ask_pipeworx for single lookups, so an agent can tell it apart 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?

It explicitly states when to use it ('Best for broad/multi-part questions over structured data') and when not to ('For a single lookup use ask_pipeworx instead'). It also covers the account/paid-tier condition and the signed-out fallback, leaving little ambiguity about tool 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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve question-answering/discovery; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all analyze prediction markets; ai_visibility_check and scan_competitor_ai_presence overlap heavily. Only the three PeeringDB search tools are clearly distinct.

Naming Consistency2/5

Naming mixes verb-led snake_case (search_networks, validate_claim, subscribe) with noun-phrase tools (entity_profile, bet_research, recent_alerts) and inconsistent prefixes (ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded; polymarket_arbitrage vs polymarket_fill_risk vs polymarket_kalshi_spread). No consistent verb_noun pattern is present.

Tool Count2/5

34 tools is heavy for a coherent surface, especially since the server is named 'Peeringdb' but only 3 of 34 tools relate to PeeringDB. The Pipeworx/Prediction-market/memory/AI-visibility tools form several distinct sub-domains that would be better split into separate servers or consolidated.

Completeness2/5

For PeeringDB, only search_exchanges/facilities/networks exist—no get-by-id, no facility/network details beyond search results, and no read/update operations. For the broader Pipeworx domain, coverage is fragmented: many meta-tools overlap while some obvious operations (e.g., updating a saved memory, deeper entity relationships) are missing. The domain is poorly scoped, making completeness hard to assess.

Resources