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Medical Codes

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?

Annotations already carry readOnlyHint, openWorldHint, idempotentHint, and destructiveHint:false, and the description adds substantial context beyond them: account/paywall requirements, parallel decomposition across 5,743 tools, findings packet structure with gaps[], contradictions[], citation_uri resolvability, semantic excerpting, and 15-90s latency expectations. This is rich behavioral disclosure.

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 dense and front-loaded with the account requirement and core capability, and nearly every sentence carries unique information. However, it is a long single block with some redundancy around citation behavior (findings packet vs the separate citation_uri caveat), so it could be tightened slightly.

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 accounts for return values: findings packet with verbatim evidence + confidence + source + fetched_at + citation, gaps[], contradictions[], hop, and citation_uri. It also covers latency, auth requirements, depth semantics, and alternative routing, so an agent has everything needed to invoke it correctly.

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% with detailed descriptions for both question and depth, so the baseline is 3. The description adds operational meaning by explaining depth behaviors ('gap recovery', 'full iterative hop', contradictions[] scan) and reinforcing that broad/multi-part natural-language questions are the intended input. This lifts it above baseline.

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 1500 STRUCTURED data sources... in ONE call'. It explicitly differentiates from open-web search and from ask_pipeworx, so an agent can tell exactly what this tool does and what it is not.

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 and when-not-to guidance: 'Best for broad/multi-part questions over structured data', 'For a single lookup use ask_pipeworx', and 'prefer ask_pipeworx' for breaking/colloquial current-news topics. It also gives an auth fallback: if not signed in, use ask_pipeworx.

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

All Medical Codes toolsInspect
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TDQS

B3.4/5.0
Disambiguation3/5

While most tools have detailed descriptions, the large number of similar data-querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research, etc.) and overlapping domains (Polymarket, company research, medical) create ambiguity for an agent.

Naming Consistency2/5

Tool names mix conventions: snake_case (ai_visibility_check, ask_pipeworx), camelCase absent, some with 'pipeworx' prefix, others not (bet_research, compare_entities). No consistent verb_noun pattern.

Tool Count1/5

33 tools for a server named 'Medical Codes' is excessive and misaligned. The vast majority of tools cover unrelated domains (finance, prediction markets, general research), making the count inappropriate for the stated purpose.

Completeness1/5

For medical coding, only three tools exist (search_icd10, search_loinc, search_medical_terms). The rest are tangential or unrelated, leaving severe gaps in medical code coverage.

Resources