Skip to main content
Glama

Data Usdot

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 (read-only, idempotent, non-destructive), the description discloses data sources (10-K items, adverse event counts), fiscal year handling, sorting by primary metric, and return of paired data with citation URIs. No contradictions.

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 relatively long but well-structured, starting with query examples and progressively adding detail. Every sentence adds value, though some consolidation could improve conciseness.

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?

Despite no output schema, the description explains what the output contains (paired data, sorted by primary metric, citation URIs) and covers main use cases. It is complete for an agent to understand tool behavior and expectations.

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?

Input schema has 100% coverage, but the description adds context: examples of values (tickers/CIKs, drug names), data sources, and how type determines data. This provides additional meaning beyond the schema definitions.

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 the tool performs side-by-side comparison of 2-5 companies or drugs, with specific data sources (SEC EDGAR for companies, FAERS/FDA for drugs). It uses action verbs and resource types, and explicitly distinguishes from sequential 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 provides explicit usage guidance: 'ALWAYS PREFER over sequential single-pack lookups' and includes example queries like 'compare X and Y', 'which is bigger', etc. It also clarifies data behavior for different entity types.

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

All Data Usdot toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Several tools have overlapping or near-identical purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are one base query flow with different flavors, while entity_profile, compare_entities, and recent_changes all overlap on company information. The Polymarket family and the discovery/onboarding tools (discover_tools, suggest_questions, pipeworx_trending) also create boundary confusion despite good descriptions.

Naming Consistency3/5

The naming is consistently snake_case and mostly readable, but it mixes verb_noun tools (compare_entities, resolve_entity, validate_claim) with noun-phrase tools (entity_profile, polymarket_arbitrage, pipeworx_trending, datasets, metadata). The ask_pipeworx variants use a beta/grounded suffix pattern that is not applied uniformly across the other tool families.

Tool Count2/5

34 tools is well above the 25+ threshold and feels like several servers merged into one: US DOT catalog access, a generic Pipeworx data router, prediction-market tools, memory/subscription management, AI-visibility checks, and meta/discovery utilities. Many tools could be consolidated (the three ask_pipeworx variants, the five Polymarket tools, and the discovery trio).

Completeness3/5

The core data lifecycle is broadly covered: catalog search, metadata, querying, natural-language lookup, grounded verification, entity resolution, entity profiles, comparison, research, subscriptions, and memory all have working paths. However, the server is named around US DOT data but only datasets/metadata/query are DOT-specific, and the rest is an unrelated general-purpose data and prediction-market toolkit, leaving the stated domain feeling incomplete.

Resources