ticker_detailsFetch full Massive (formerly Polygon.io) reference details for a single ticker: company name, description, SIC code, primary exchange, locale, market cap, phone, address, homepage URL, and logo.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | API response status | |
| results | No | Ticker details |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds behavioral context by listing the exact fields returned, which goes beyond annotations, but it does not cover edge cases like missing tickers or data latency. This is a meaningful but not exhaustive addition.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the action and enumerates key fields without unnecessary words. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with an output schema and comprehensive annotations, the description covers purpose, parameter semantics, and expected output fields. It lacks alternative usage guidance, but that is addressed in the usage guidelines dimension. Overall, it provides sufficient information for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter ('ticker') with no description (0% coverage). The description compensates by stating 'single ticker', clarifying that exactly one symbol is expected, and the schema example 'AAPL' provides format context. However, it does not elaborate on valid formats or constraints, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') and resource ('full Massive reference details for a single ticker'), and enumerates the fields returned (company name, description, SIC code, etc.). This clearly distinguishes it from sibling tools like 'tickers' (plural) and 'entity_profile' by emphasizing 'single ticker'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this for a single ticker's reference details. However, it does not explicitly name when to prefer this over alternatives like 'tickers' or 'entity_profile', nor does it state exclusions, so it lacks explicit alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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-*'.
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.
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.