Polygon Io

Daily Open Close

daily_open_close

Read-onlyIdempotent

Fetch official open, high, low, close, volume, and after-hours/pre-market prices for a US stock ticker on a specific date (YYYY-MM-DD) from Massive (formerly Polygon.io).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`date`	Yes
`ticker`	Yes
`adjusted`	No

Output Schema

TableJSON Schema

Name	Required	Description
`low`	No	Low price
`from`	No	Date of data
`high`	No	High price
`open`	No	Opening price
`close`	No	Closing price
`status`	No	API response status
`symbol`	No	Ticker symbol
`volume`	No	Trading volume
`preMarket`	No	Pre-market price
`afterHours`	No	After-hours price

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior. The description adds context about the data source (Massive/Polygon.io) and the inclusion of after-hours/pre-market prices, which helps the agent understand the tool's scope without contradicting annotations.

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 a single, well-structured sentence that is concise and front-loaded. It includes essential details without any fluff or redundancy.

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?

The tool is relatively simple, and the output schema covers return values. The description adequately covers the core purpose and data scope, but it omits the adjusted parameter and any guidance on alternatives, so it is not fully complete.

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 coverage is 0%, so the description must compensate. It explains the date format (YYYY-MM-DD) and specifies that the ticker is for US stocks, but it does not explain the 'adjusted' parameter, leaving an important gap for agents deciding how to use optional parameters.

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 fetches official open, high, low, close, volume, and after-hours/pre-market prices for a US stock ticker on a specific date. This distinguishes it from sibling tools like previous_close or aggregates, which serve different data needs.

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 for when to use the tool: when daily official OHLCV data including after-hours/pre-market for a single ticker and date is needed. It does not explicitly name alternatives, but the specificity implies the appropriate use case.

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

Disambiguation3/5

Tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded have overlapping purposes (all answer questions via a universal router), with only subtle distinctions (beta version, grounded mode). Additionally, many tools like entity_profile, compare_entities, recent_changes, and resolve_entity all pull SEC/company data, and polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, bet_research all relate to prediction markets, creating potential confusion. However, each tool does have a somewhat distinct purpose and detailed descriptions help differentiate them, so it's not extreme overlap.

Naming Consistency2/5

Tool names are mostly lowercase with underscores (e.g., 'ask_pipeworx', 'compare_entities', 'resolve_entity'), but there's a mix of verb-first (bulk_splits, list_subscriptions) and noun-first (data_types, get_quote) patterns. Also 'aggregates' and 'grouped_daily' both fetch bars but have different naming styles. The naming is inconsistent with no clear uniform pattern, and some names are vague like 'helpers' or 'utility-*'.

Tool Count2/5

43 tools is quite heavy for a single MCP server, exceeding the typical 15-25 range for 'too many'. While the server aggregates many different domains (Polygon stocks, Pipeworx data, Polymarket, npm, etc.), the sheer number makes it overwhelming for an agent to discover and select the right tool. Many tools are meta-tools (ask_pipeworx, discover_tools) that add complexity rather than mapping to a clear domain.

Completeness4/5

The server covers a huge range of operations: stock data (retrieve, search, details), prediction markets (arbitrage, edges, research, fill risk), entity resolution, subscriptions, memory, and meta-tools. There are some gaps like no obvious tool for modifying stock data (not expected) and the Polymarket side lacks a tool for placing actual trades or managing positions. But overall the surface is quite complete for a comprehensive data/research server.

Daily Open Close

Input Schema

Output Schema

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