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Glama

Statbank Md

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 1500 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 5,743 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.

TDQS

A4.8/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: requires an account (and paid plan for 'thorough'), returns a findings packet with verbatim evidence + confidence + source + fetched_at + pipeworx:// citation, includes gaps[] for unanswered facets, contradictions[] for standard/thorough, a hop field, and excerpting behavior. It also discloses latency (15-60s, up to ~90s) and the limitation that news topics yield mostly empty gaps. No contradiction with readOnlyHint/openWorldHint/idempotentHint.

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 every sentence carries information: account gate, usage context, return format, limitations, hop behavior, latency. It is front-loaded with the critical account requirement and the distinction from open-web search. While dense, it avoids repetition and organizes information in a logical flow. Slightly verbose but justified for a complex tool.

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 without an output schema, the description fully compensates by describing the return shape (findings packet, gaps[], contradictions[], hop, citation_uri), explaining edge cases (news topics, large records excerpting), and setting expectations (latency, account tiers). It also names the sibling tool (ask_pipeworx) and the conditions that select each. Nothing an agent needs to decide or call correctly 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?

The schema covers 100% of parameters, so the baseline is 3, but the description adds meaningful semantics beyond the enum descriptions: it explains the practical effect of each depth level ('standard' re-angles unanswered gaps, 'thorough' additionally chases the best leads) and that 'question' supports broad/multi-part queries. It clarifies the trade-offs between levels (e.g., 'thorough' is paid and slower).

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 states a specific verb ('researches'), a precise resource ('Pipeworx's 1497 STRUCTURED data sources'), and its core mechanism ('decomposes your question into focused facets, routes each to the right one of 5,724 tools IN PARALLEL'). It explicitly differentiates from ask_pipeworx ('For a single lookup') and clarifies it is NOT open-web search. This clearly distinguishes it from all 30+ 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?

Explicitly states when to use this tool vs. alternatives: 'For a single lookup use ask_pipeworx', and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx — it routes to live news APIs'. It also describes depth levels and when broad/multi-part questions are appropriate. This is example-level guidance with named alternatives.

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

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in scope, and the prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) share similar functions. While some tools are distinct, the boundaries between many are unclear.

Naming Consistency2/5

Names mix product-like identifiers (ask_pipeworx, deep_research), descriptive nouns (entity_profile, subjects), and inconsistent verb forms (query_table, resolve_entity, scan_competitor_ai_presence). No consistent verb_noun pattern is maintained across the set.

Tool Count2/5

With 34 tools, the server is overpopulated relative to its apparent purpose. The name 'Statbank Md' suggests a narrow statistical service, but only 3 tools are Statbank-specific; the rest form a sprawling general-purpose data toolkit. The count is far beyond what the core function needs.

Completeness3/5

For the Statbank subset, the surface is complete (browse subjects, get metadata, query data). As a general data research suite, it covers many domains (SEC, FDA, economics, prediction markets) but lacks execution/trading tools for prediction markets and has no bulk data export or analytics beyond excerpts. Notable gaps exist but many core workflows are covered.

Resources