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Glama

Realestateapi

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

A4.9/5.0
Behavior5/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) already signal safety, and the description layers substantial behavior on top: parallel decomposition/routing, explicit gaps[] meaning 'never invented', hop field semantics, citation_uri being 'present only when the source emits one that resources/read can actually serve', contradictions[] on standard/thorough, and semantic excerpting to facet-relevant passages rather than head-truncation. It also discloses the account/paid-plan gate and latency expectations (15-60s, ~90s for thorough).

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 (~250 words) but nearly every sentence earns its place given the tool's complexity — depth tiers, an account gate, an alternative tool, and an undocumented return shape. It is front-loaded with the most critical gate (account required) and the key identity statement ('NOT open-web search'). Minor redundancy keeps it from a 5: the ask_pipeworx routing advice appears twice and gaps[] behavior is explained in two separate places.

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?

With no output schema, the description fully carries the return-value burden — findings packet contents (verbatim evidence + confidence + source + fetched_at + pipeworx:// citation), gaps[], contradictions[], hop field, and citation_uri resolvability. It also covers param semantics, latency, account requirements, and multi-step iteration, leaving no operational question an agent needs answered to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds meaning the schema lacks: it quantifies depth tiers ('quick=3', 'standard=3 (default)', 'thorough=6 (paid)'), explains what the gap-recovery hop and iterative hop actually do per tier, and clarifies that contradictions[] only appear on standard/thorough. For 'question' it validates the intended use ('Broad/multi-part is fine — decomposition is the point') and steers single-lookups to ask_pipeworx, which the raw schema never hints at.

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 names a specific verb ('research'), a concrete resource ('Pipeworx's 1517 STRUCTURED data sources'), and a distinctive behavior (decomposes into facets, routes in parallel to 5,798 tools, returns a findings packet). It also draws a hard line against confusion with open-web search ('this is NOT open-web search') and contrasts itself with the sibling ask_pipeworx, so an agent can disambiguate without inspecting other schemas.

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?

Usage guidance is explicit and actionable: 'If you are not signed in, use ask_pipeworx instead,' 'Best for broad/multi-part questions over structured data,' 'For a single lookup use ask_pipeworx,' and a specific exclusion for BREAKING/current-news topics where ask_pipeworx routes to live news APIs and deep_research would return 'mostly empty gaps[]'. It even stratifies depth tiers (standard vs thorough) so the agent can pick the right variant.

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

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research and ask_pipeworx both answer broad factual questions, and the polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, bet_research) cover heavily overlapping edge/arb research territory. An agent could easily route a query to the wrong one.

Naming Consistency2/5

Most tools use snake_case, but there is no consistent verb_noun pattern: ask_pipeworx, deep_research, bet_research, recent_changes, remember/recall/forget, generate_llms_txt, realestateapi_property_detail, and polymarket_edges all follow different structural conventions. The server name Realestateapi also does not match the broader Pipeworx/polymarket tool set.

Tool Count2/5

34 tools is above the 25+ threshold for a heavy, hard-to-navigate surface, especially for a server named Realestateapi where only 3 tools actually concern real estate. Many tools are generic utilities, memory helpers, feedback channels, and prediction-market tooling that feel unrelated to the apparent real-estate API scope.

Completeness2/5

For a real-estate-focused server, the surface is significantly incomplete: property search, property detail, and skip-trace cover only basic owner/value lookups. Missing obvious real-estate capabilities like comparable sales, tax history, market trends, school/flood data, and listing lifecycle operations create notable gaps an agent would need to work around.

Resources