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LiveDataLink

by io.github.blackboxfoundry

stock_compare

Read-onlyIdempotent

Compare 2 to 5 stocks side by side. Returns price, daily change, market cap, P/E ratio, dividend yield, volume, 52-week range, sector, revenue, profit margin, EPS, and beta. Use this for "compare Apple and Microsoft", "which is a better investment, NVDA or AMD?", "tech stock comparison", or any stock-vs-stock analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolsYesTicker symbols, 2-5 stocks. Accept either CSV string ("AAPL,MSFT,GOOGL") or array (["AAPL","MSFT","GOOGL"]).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish this as read-only, idempotent, and non-destructive. The description adds useful behavioral context by constraining the input to 2-5 stocks and disclosing exactly which metrics are returned, which is especially valuable given there is no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two focused sentences: the first states what the tool does and returns, the second gives real user-intent examples. There is no filler or redundancy.

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?

With only one parameter and rich schema coverage plus safety annotations, this description is complete enough for an agent to select and invoke the tool correctly. The returned-metric list compensates for the lack of an output schema, and the examples clarify the intended use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents the single 'symbols' parameter thoroughly, including the 2-5 limit and both CSV string and array formats. The description repeats the count but does not add meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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 names a specific action ('compare') and resource ('2 to 5 stocks'), plus a detailed list of returned metrics. This clearly distinguishes it from single-stock quote tools like stock_quote.

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 several concrete example queries and a general rule ('any stock-vs-stock analysis'), making intended usage easy to recognize. It does not explicitly name alternatives such as stock_quote_batch or state when not to use it, so it stops short of a full 5.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources