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simplefunctions

by io.github.spfunctions

query_databento

Free-form historical market data query via Databento. Stocks, ETFs, CME futures (WTI, Brent, bonds, VIX, FX, BTC), options. OHLCV daily/hourly/minute, trades, BBO. Max 30 days, 5 symbols, 500 rows. Free-tier and rate-limited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback days (default 7, max 30)
stypeNoraw_symbol (default) or continuous (for .FUT symbols)
schemaNoohlcv-1d (default), ohlcv-1h, ohlcv-1m, trades, bbo-1s, bbo-1m, statistics, definition
datasetNoDBEQ.BASIC (stocks/ETFs, default), GLBX.MDP3 (CME futures), OPRA.PILLAR (options), XNAS.BASIC (Nasdaq)
symbolsYesComma-separated symbols (max 5). Use .FUT for continuous futures. Examples: SPY, CL.FUT, ES.FUT, GLD

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behavioral constraints: max 30 days, 5 symbols, 500 rows, and free-tier rate limits. It also lists supported data schemas, giving the agent insight into the response nature. It doesn't explicitly state 'read-only', but 'query' implies it.

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?

The description is three sentences with no filler: it states the purpose, supported assets/schemas, and constraints. It is front-loaded with the core intent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While no output schema exists, the description conveys the type of data returned (OHLCV, trades, BBO) and limits, which is fairly complete for a query tool. It doesn't detail response format or error handling, but given the tool's moderate complexity, it covers the essential aspects.

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 has 100% coverage, so the baseline is 3. The description adds some context about data types and symbols (e.g., CME futures, options) that align with schema parameters, but it largely reiterates the schema's own descriptions, adding minimal new parameter-specific meaning.

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 identifies the tool as a historical market data query via Databento, and enumerates asset classes and data schemas. This distinguishes it from sibling tools like query_econ or scan_markets.

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 provides clear context on the tool's scope (stocks, futures, options, OHLCV, trades, BBO) and sets expectations with limits and rate limiting. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to know when to use it.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple market query tools (scan_markets, screen_markets, get_market_detail, get_market_diff, get_market_history, inspect_ticker) and legislative tools (legislation, get_legislation, list_legislation, query_gov). Aliases like get_heartbeat_config/get_heartbeat_status and explore_public/explore_theses add further confusion. An agent would struggle to select the correct tool without deeply reading each description.

Naming Consistency3/5

Most tools follow a verb_noun pattern (get_, list_, create_, update_), but there are notable deviations: 'legislation' lacks the 'get_' prefix, 'stt' and 'tts' are acronyms, 'monitor_the_situation' is a full phrase, and 'x_account/x_news/x_volume' use a non-standard prefix. The overall style is readable, but the mixed conventions reduce predictability.

Tool Count1/5

108 tools is extreme for any server, even one covering prediction markets, trading, portfolio management, forum, skills, and speech. The massive surface area overwhelms agents and makes the server feel more like a platform than a coherent toolkit. This many tools inevitably leads to redundancy and maintenance burden.

Completeness4/5

The server covers an impressively broad domain: market data, thesis management, intents, strategies, positions, portfolio, forum, skills, legislative and economic queries, and audio/visual processing. Minor gaps exist (e.g., no delete for skills/theses, no update for some portfolio items) but core workflows are well-supported. Overall lifecycle coverage for most entities is strong.

Resources