chart_data"Chart data for [ticker]" / "sparkline data for [stock]" — price sparkline data for a ticker from StockTwits. Use for embedded thumbnail charts; prefer dedicated finance packs (yahoo-finance, alphavantage) for serious price analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | ||
| symbol | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the tool's safety profile is clear. The description adds context about data source (StockTwits) and intended use (thumbnail charts), which is useful but not critical beyond annotations.
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?
Two sentences with high information density: purpose, data source, use case, alternatives. Front-loaded with calling conventions. No wasted words.
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 two-parameter tool with rich annotations, the description covers purpose, usage context, and alternatives. However, it lacks any description of return value format or data granularity, which would be helpful given no output schema.
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?
Schema coverage is 0%, so description should compensate. It implicitly documents the 'symbol' parameter via examples ('[ticker]'), but does not explain the 'period' parameter at all. Meaning added is minimal for the optional parameter.
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 clearly states it provides 'price sparkline data for a ticker from StockTwits' and gives example usage patterns. It explicitly differentiates from sibling tools by naming alternatives (yahoo-finance, alphavantage) for serious price analysis.
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?
Explicitly states when to use ('embedded thumbnail charts') and when not to ('prefer dedicated finance packs...'). Names specific alternatives, giving clear context for agent decision-making.
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.
Multiple tools serve overlapping functions: three ask_pipeworx variants, two deep research tools, and multiple polymarket tools. StockTwits-specific tools are distinct but the overall set has significant ambiguity between Pipeworx and Polymarket tools.
Naming styles are mixed: some use lowercase (ask_pipeworx), some use underscores (ai_visibility_check), and some use prefixes (pipeworx_feedback, polymarket_arbitrage). No consistent pattern across the tool set.
40 tools is on the higher end but not extreme. However, many tools belong to the Pipeworx ecosystem, which seems separate from StockTwits, making the set feel bloated and unfocused for a single server.
StockTwits social features are adequately covered (symbol search, streams, trending), but the inclusion of Pipeworx tools creates a sprawling surface without complete coverage in any one domain. Missing core StockTwits features like user profiles or direct messaging.