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Polygon Io

by io.github.pipeworx-io

Grouped Daily

grouped_daily
Read-onlyIdempotent

Fetch OHLCV bars for all US stocks on a given date (YYYY-MM-DD) from Massive (formerly Polygon.io) in a single call; useful for market-wide snapshot or screening.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes
adjustedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of results
statusNoAPI response status
resultsNoDaily data for all tickers
field_legendNoLegend for the single-letter OHLC bar keys (o/h/l/c/v/vw/n/t).

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds useful operational context beyond those: it fetches all US stocks in one call, requires a date, and sources data from Massive/Polygon.io. It does not mention potential response size or rate limits, but the output schema and annotations cover the main safety and return expectations.

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 front-loads the action and resource, then adds the parameter format and a use case. Every clause contributes value with no 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?

Given the output schema, annotations, and modest two-parameter surface, the description adequately covers the tool's purpose, universe, and date format. The only notable omission is the adjusted parameter semantics, but the overall context is sufficient for an agent to select and invoke this read-only bulk data tool.

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

Parameters2/5

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

With 0% schema description coverage, the description must compensate for parameter explanations. It does document the date format (YYYY-MM-DD), but the 'adjusted' boolean parameter is never mentioned or explained, leaving a meaningful gap in usability.

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 action (Fetch OHLCV bars), resource (all US stocks on a given date from Massive/Polygon.io), and format (YYYY-MM-DD). It distinguishes itself from sibling tools like aggregates or daily_open_close by emphasizing the market-wide, single-call nature.

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 phrase 'useful for market-wide snapshot or screening' provides clear context for when to choose this tool. It does not explicitly name alternatives or state when not to use it, but the market-wide scope strongly implies a contrast with single-ticker endpoints.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Polygon Io toolsInspect
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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.

Resources