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Drillr — The financial MCP for AI agents

by ai.drillr

list_tables

Read-only

List alternative-data tables under the given categories. Returns each table's name, one-line purpose, and column names (call get_table_schema if you need column types/comments). Batch up to 5 categories in one call; omit categories, or pass ["all"], to get the category index instead.

Use this BEFORE run_sql when you want to explore alt-data — run_sql alone won't tell you which tables exist.

Available categories:

  • Energy & Power — US power plants, electricity prices, regional hourly generation/demand

  • Data Centers — facilities, GPU clusters, cooling

  • Semiconductors — AI chip specs, sales, ownership, foundry revenue, customs trade

  • Compute Pricing — GPU rental, cloud VM spot/on-demand, instance specs

  • Model Development — model specs, benchmarks, AI companies, AI polling, LLM arena

  • Inference Economics — LLM API pricing across providers

  • Macro & Trade — UN Comtrade, US Census trade flows, FRED macro series

  • Prediction Markets — Polymarket and Kalshi events, markets, trades, daily aggregates

  • Critical Minerals — USGS mineral deposits, country supply, critical materials

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoriesNoAltdata category names (see tool description for the list). Omit, or pass "all", for the category index.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds batching limit (up to 5 categories), behavior when omitted or 'all', and what is returned (name, purpose, column names). No 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?

Well-structured with purpose first, then details, usage guidance, and categories. Slightly long but every sentence serves a purpose. Could be slightly more concise but effective.

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?

No output schema, but description covers what is returned (name, purpose, column names) and directs to get_table_schema for types. Includes batching and categories. Complete for a listing tool.

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 enum list and description. Description reinforces the categories list and adds batching instructions and behavior when omitted. Adds value beyond schema, so slightly above baseline 3.

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?

Clearly states 'List alternative-data tables under the given categories' with specific verb and resource. Distinguishes from siblings like get_table_schema and run_sql by explaining what each returns and when to use them.

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 says 'Use this BEFORE run_sql when you want to explore alt-data' and mentions get_table_schema for more details. Provides clear when-to-use and alternatives.

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

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: company_search is qualitative discovery, run_sql is quantitative querying, news_search covers news, sec_report_list/search handle filings, ticker_lookup resolves identifiers, and list_tables/get_table_schema/fiscal_utility support exploration. Cross-references between tools (e.g., company_search explicitly defers to run_sql) prevent confusion.

Naming Consistency4/5

Most tools follow a predictable noun_verb or noun_noun pattern (company_search, news_search, ticker_lookup, run_sql, list_tables, get_table_schema). sec_report_list and sec_report_search are clearly related with descriptive suffixes. Minor deviation: fiscal_utility is vaguer than its siblings, but the pattern is otherwise consistent.

Tool Count5/5

9 tools is right-sized for a financial data platform — enough for comprehensive coverage (search, query, schema, news, filings) without redundancy. Each tool serves a distinct purpose and the count fits the domain well.

Completeness5/5

The tool set forms a coherent workflow: ticker_lookup → company_search/run_sql for fundamentals, news_search for events, sec_report_list/search for filings, and list_tables/get_table_schema for schema discovery. The only minor gap is the absence of a direct 'company profiles' tool separate from company_search, but run_sql covers quantitative needs adequately. The surface is complete for a read-only financial analysis MCP.

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