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Datalastic

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"]).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and idempotent, but the description adds substantial context: parallel execution, per-type data sources (SEC EDGAR/XBRL vs. FAERS/FDA), off-calendar fiscal year handling, sorting by primary metric, and citation URIs. No contradictions 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 dense and front-loaded with trigger phrases, and every sentence adds meaningful information. However, it is a single run-on paragraph; a bulleted structure would improve scannability without sacrificing content.

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?

Even without an output schema, the description covers return format (paired data + citation URIs), metric sources per entity type, entity count limits, and sorting behavior. This is complete for the tool's complexity and leaves no critical gaps for the agent.

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?

Schema coverage is 100%, but the description goes beyond the schema by explaining what each type ('company' vs 'drug') returns, providing example values, and clarifying output sorting. This gives the agent enough to construct valid calls with confidence.

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 explicitly states the tool performs side-by-side comparison of 2–5 companies or drugs in one parallel call, with specific trigger phrases like 'X vs Y' and 'rank these companies.' This clearly distinguishes it from sibling tools such as entity_profile or 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when to use the tool (comparison queries) and explicitly says to prefer it over sequential single-pack lookups. However, it does not name a specific alternative tool or state when not to use it (e.g., for single-entity lookups).

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
Disambiguation2/5

Several tool clusters have genuinely fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grouned, and deep_research all route to the same 5,767 tools and differ only by use-case nuance, while polymarket_edges, polymarket_arbitrage, and bet_research all surface trading opportunities. scan_competitor_ai_presence is a thin wrapper over ai_visibility_check, and entity_profile, recent_changes, and compare_entities pull overlapping company data. The descriptions are detailed, but an agent can easily select the wrong tool in these clusters.

Naming Consistency4/5

All tools use snake_case and family prefixes are consistent (polymarket_*, pipeworx, datalastic_*, scan_*, ask_*), making the set predictable and readable. The main deviation is verb placement — verb-first (list_subscriptions, resolve_entity, search_within) vs noun-first (entiy_profile, recent_alerts, bet_research) — and prefix position varies between ask_pipeworx and pipeworx_feedback, but these are minor.

Tool Count2/5

33 tools exceeds the heavy threshold, and the count is padded by redundancy: four ask_pipeworx variants that are near-identical, six polymarket tools with overlapping scans, and wrapper tools like scan_competitor_ai_presence that just call ai_visibility_check. The server name suggests maritime focus but only two tools serve that domain, while the rest span a sprawling data-research, prediction-market, AI-visibility, and npm-scanning surface. Consolidating the ask_pipeworx family into one router with a mode parameter and merging wrappers would trim the set to roughly 20 tools without losing capability.

Completeness4/5

The core data-research lifecycle is thoroughly covered: resolve_entity feeds entity_profile, compare_entities, recent_changes, validate_claim, and deep_research, and the prediction-market workflow includes discovery, edge detection, fill-risk validation, and cross-venue analysis. Subscriptions, memory, and feedback are well supported. Minor gaps exist — the datalastic maritime piece has only live position lookups (no history or fleet tools), and one-offs like generate_llms_txt and scan_dependency feel unrelated — but there are no critical dead ends.

Resources