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Nice-Wolf-Studio

DataBento MCP Server

by Nice-Wolf-Studio
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

timeseries_get_range

Fetch historical market data for any supported schema (e.g., trades, OHLCV) by specifying dataset, symbols, and date range.

Instructions

Get historical market data with flexible schemas and date ranges. Supports all Databento schemas (mbp-1, mbp-10, trades, ohlcv-1h, ohlcv-1d, etc.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetYesDataset code (e.g., 'GLBX.MDP3' for CME, 'XNAS.ITCH' for Nasdaq)
symbolsYesComma-separated list of instrument symbols (up to 2000)
schemaYesData schema type
startYesStart date (ISO 8601 or YYYY-MM-DD format)
endNoEnd date (ISO 8601 or YYYY-MM-DD format), defaults to start date
stype_inNoInput symbology type, defaults to 'raw_symbol'
stype_outNoOutput symbology type, defaults to 'instrument_id'
limitNoMaximum number of records to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description only states basic functionality. Lacks details on side effects, rate limits, pagination, or read-only nature. Agent misses crucial behavioral context.

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?

Two concise sentences, first sentence states purpose, second provides example schemas. Efficient but could be slightly more front-loaded.

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

Completeness2/5

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

With 8 parameters (4 required) and no output schema, the description is too minimal. Fails to explain required inputs like dataset, symbols, start, or expected output format.

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?

Schema description coverage is 100%, so the schema already documents all parameters. Description adds little beyond listing schema examples, meeting the baseline but no extra value.

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 states the tool retrieves historical market data and lists supported schemas, effectively distinguishing it from sibling tools like batch operations or metadata queries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like get_historical_bars or batch_download. Does not specify prerequisites or exclusions.

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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