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Magic 8 Ball

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

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description reveals data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and output format with citation URIs. This provides critical behavioral context.

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 appropriately detailed for the complexity of the tool. It front-loads common queries and then explains key details. Every sentence is informative, though it could be slightly more structured (e.g., separate sections for company vs drug).

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 simple schema (2 params with 100% coverage), rich annotations, and no output schema, the description fully covers input examples, data sources, edge cases (fiscal year handling), sorting behavior, and output format. It leaves no obvious gaps for an agent to misunderstand.

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% with descriptions for both parameters, but the description adds significant value: explains what type='company' and type='drug' retrieve, gives examples for values, and clarifies constraints (tickers/CIKs vs drug names, max 5 items).

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 starts with multiple natural language query examples and clearly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call'. It distinguishes from sequential single-entity lookups, making the purpose unmistakable.

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 advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. The context also implies when not to use (for single entities) without needing to state it.

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

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the three ask_pipeworx variants (standard, beta, grounded) share similar routing and could cause confusion for an agent. The memory tools and novelty tool are distinct. Overall, ambiguity is low.

Naming Consistency4/5

Tool names follow a consistent snake_case verb_noun pattern (e.g., ask_pipeworx, compare_entities, subscribe). Minor deviations like 'search_within' and 'magic_8_ball_ask' do not break the pattern. High consistency.

Tool Count3/5

With 32 tools, the server is on the heavier side for a single MCP server. However, given the broad domain coverage (financials, economics, prediction markets, etc.), each tool serves a distinct purpose. Bordering on too many, but justified by scope.

Completeness4/5

The tool surface covers a comprehensive range of data research operations: querying, deep research, entity profiling, comparisons, discovery, subscriptions, alerts, claim validation, and prediction market analysis. Minor gaps exist (no data modification tools), but they are out of scope for a query-oriented server.

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