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Clinicaltables

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false), the description adds details on data sources, handling of off-calendar fiscal years, result sorting by primary metric, and return of citation URIs. No contradiction with annotations.

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 comprehensive and front-loaded with example queries and a clear directive to prefer over sequential lookups. While it is slightly verbose, every sentence adds value, making it effective.

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 tool's complexity (two entity types, multiple financial metrics, no output schema), the description covers all necessary aspects: data sources, handling of fiscal years, sorting, output format (paired data + URIs), and replacement of many sequential lookups. It is complete for agent invocation.

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?

The input schema already provides descriptions for both parameters (type with enum, values with constraints). The description adds further context on how to specify values (tickers for companies, drug names for drugs) and what data each type retrieves, enhancing clarity.

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 states that the tool performs side-by-side comparisons of 2–5 companies or drugs in one call, using specific data sources (SEC EDGAR for companies, FAERS for drugs). It includes example queries and distinguishes itself from sequential single-pack lookups.

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 instructs to always prefer this tool over sequential lookups when comparing entities. It provides context on when to use it (comparison queries) and explains the data returned for each type, making the usage clear.

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

A3.9/5.0
Disambiguation3/5

Several tool groups (ask_pipeworx/ask_pipeworx_grounded/deep_research, conditions/disease_names, polymarket_* family) have overlapping purposes, requiring careful reading of descriptions to differentiate. While many tools are conceptually distinct, the similarity within domains could lead to agent misselection.

Naming Consistency3/5

Naming is mostly snake_case but patterns vary: verb_noun (ask_pipeworx), noun_noun (entity_profile), single verb (forget, recall), and some adjectives (recent_changes). No consistent structure across the set, though individual names are descriptive.

Tool Count2/5

With 40 tools, the server is overly large for typical coherence. The tool count exceeds the 15–25 range deemed borderline, making it hard for agents to navigate and select efficiently.

Completeness4/5

The surface covers medical coding (ICD, LOINC, UCUM, drugs, procedures), financial data, prediction markets, memory, subscriptions, and meta-tools. Missing but minor elements (e.g., CPT codes) do not significantly hinder common workflows.

Resources