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Polygon Io

by io.github.pipeworx-io

Splits

splits
Read-onlyIdempotent

Historical stock splits for a US-listed ticker, from Massive (formerly Polygon.io): split ratio, execution date, ticker. Use to adjust historical price comparisons across split events.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
tickerNo
execution_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of results
statusNoAPI response status
resultsNoStock splits data
next_urlNoNext page URL if available

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds context about the data source and the purpose (adjusting historical price comparisons), but does not disclose details like pagination, rate limits, or data coverage limitations beyond 'US-listed ticker'.

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 two sentences, front-loaded with the core purpose and data source, and ends with a clear use case. Every word earns its place; no fluff.

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 tool's simplicity (3 optional parameters, no required fields) and the presence of an output schema, the description is fairly complete. It explains the data source, the fields, and the use case. However, it could mention that all parameters are optional (since none are required) and clarify the 'limit' parameter's role, but the output schema likely covers return structure.

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 0%, so the description must compensate. It mentions 'split ratio, execution date, ticker' as fields, which maps to the 'ticker' and 'execution_date' parameters, but does not explain the 'limit' parameter or provide format details (e.g., date format). The description adds some meaning but leaves gaps for the 'limit' parameter and date format.

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 provides historical stock splits for a US-listed ticker, specifying the data source (Massive, formerly Polygon.io) and the key fields (split ratio, execution date, ticker). It distinguishes itself from siblings like 'dividends' and 'aggregates' by focusing on split events.

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 explicitly states the use case: 'Use to adjust historical price comparisons across split events.' It implies when to use this tool (when needing split data) but does not explicitly mention when not to use it or alternatives, though the sibling list includes related tools like 'dividends' and 'aggregates'.

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.

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