plot_financial_lineGenerate and plot synthetic financial price data (requires matplotlib).
Creates realistic price movement patterns for educational purposes. Does not use real market data.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days to generate, e.g., 30 | |
| color | No | Line color (name or hex code, e.g., 'blue', '#2E86AB') | |
| trend | No | Market trend direction | bullish |
| start_price | No | Starting price value, e.g., 100.0 |
Changes observed during successful MCP inspections.
Input schema / properties / start_price / descriptionAdded value: +"Starting price value, e.g., 100.0"Input schema / properties / trend / descriptionAdded value: +"Market trend direction"Input schema / properties / trend / examplesAdded value: +[
+ "bullish",
+ "bearish",
+ "volatile"
+]Input schema / properties / color / descriptionAdded value: +"Line color (name or hex code, e.g., 'blue', '#2E86AB')"Input schema / properties / days / descriptionAdded value: +"Number of days to generate, e.g., 30"Output schema / (root)Previous value: -{
- "additionalProperties": true,
- "type": "object"
-}New value: +nullInput schema / additionalPropertiesAdded value: +falseInput schema / properties / color / titleRemoved value: -"Color"Input schema / properties / days / titleRemoved value: -"Days"Input schema / properties / start_price / titleRemoved value: -"Start Price"Input schema / properties / trend / titleRemoved value: -"Trend"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and idempotent behavior, so the bar is lower, and the description still adds genuinely useful context: the matplotlib dependency and the explicit warning that the data is synthetic rather than real. That said, it says nothing about how the chart is delivered (returned inline vs. saved to a file), which matters for a plotting tool with no output schema.
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?
Three short sentences with the core action front-loaded and no filler. The matplotlib caveat is placed parenthetically rather than buried, though the dependency could arguably be its own sentence for scannability.
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?
With no output schema and no annotations about output, the description should say what the caller receives (image, file path, or data). It covers inputs and the synthetic-data caveat adequately but leaves the return contract entirely unspecified.
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 description coverage is 100%, so all four parameters (days, color, trend, start_price) are already documented with defaults and examples in the schema. The description adds no parameter-level meaning beyond that, so the baseline 3 applies.
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?
States a specific verb and resource ('Generate and plot synthetic financial price data') and pins the content domain to financial prices, which separates it from the generic plot_line_chart sibling. It does not explicitly name which sibling to prefer when the data isn't financial, but the domain qualifier does most of the disambiguating work.
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?
Gives implied context ('for educational purposes') and a negative constraint ('does not use real market data'), which tells the agent not to reach for this tool when actual market data is needed. However, it never states when to choose this over plot_line_chart or plot_function, nor any prerequisite beyond the matplotlib note.
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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