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Datos Co

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: results sorted by primary metric, correct handling of off-calendar fiscal years, and return of citation URIs per entity. This goes beyond annotations without contradiction.

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 a single paragraph that efficiently packs in multiple usage examples, data details, and behavioral notes without fluff. Every sentence adds value, though a bullet structure could improve scanability.

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?

For a tool with two entity types, multiple data sources, and no output schema, the description fully explains what data is returned (4 financial metrics for companies, 3 metrics for drugs), sorting behavior, and citation format. It is complete given the tool's complexity.

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%, so baseline is 3. The description adds real-world examples ('[AAPL,MSFT]', 'ozempic,mounjaro') and clarifies the min/max constraints, providing practical meaning beyond the schema descriptions.

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 specific example queries like 'Compare X and Y' and 'rank these companies,' clearly stating the tool does side-by-side comparison of 2-5 entities in one parallel call. It distinguishes itself from sequential single-pack lookups, making the purpose unambiguous.

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 explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear when-to-use guidance. It also explains the data sources for each type but could briefly mention that single-entity lookups are better for non-comparative queries.

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

Most tools have distinct purposes, but several natural-language query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) occupy overlapping territory, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also has fuzzy boundaries, though the long descriptions help an agent differentiate.

Naming Consistency3/5

Names are consistently lowercase with underscores, and there are coherent subfamilies like ask_pipeworx*, polymarket_*, and scan_*. However, the overall set mixes conventions: verb_noun (query_dataset, resolve_entity), noun_noun (entity_profile, bet_research), adjective_noun (recent_changes), and bare verbs (remember, recall, forget), so no single predictable pattern governs the server.

Tool Count2/5

34 tools is a heavy surface for one server, including multiple meta/onboarding utilities (discover_tools, suggest_questions, pipeworx_feedback, pipeworx_trending) and a large prediction-market subcluster. The count exceeds the 25-tool threshold where a tool set typically becomes unwieldy, and several tools could be consolidated or split into separate servers.

Completeness4/5

For a read-heavy data research platform, the surface is very complete: discovery, routed lookup, grounded verification, entity resolution, profiles, comparisons, change feeds, dataset querying, prediction-market research, and full memory/subscription lifecycles are all covered. Minor gaps exist, such as no explicit pipeworx:// URI read tool and no subscription update operation, but agents can work around them.

Resources