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plot_math_function

Generate 2D plots of mathematical functions using Desmos API or local rendering to visualize equations with customizable x and y ranges.

Instructions

Generates a 2D plot. Tries Desmos API if available, otherwise falls back to local rendering.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formulaYes
x_rangeNo
y_rangeNo
use_apiNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'plot_math_function' tool. It handles plotting using Desmos API if available, falling back to local SymPy rendering, and returns a base64-encoded image URI.
    @mcp.tool()
    async def plot_math_function(
        ctx: MCPContext,
        formula: str,
        x_range: Optional[List[float]] = [-10, 10],
        y_range: Optional[List[float]] = None,
        use_api: bool = True
    ) -> str:
        """Generates a 2D plot. Tries Desmos API if available, otherwise falls back to local rendering."""
        await ctx.progress.start(message="Initializing plot...")
        
        if use_api and desmos_client:
            ctx.info("Attempting to plot with Desmos API...")
            await ctx.progress.report(1, 2, "Calling Desmos API...")
            try:
                image_bytes = await desmos_client.plot_formula(formula, x_range, y_range)
                if image_bytes:
                    await ctx.progress.report(2, 2, "Encoding image...")
                    img_base64 = base64.b64encode(image_bytes).decode('utf-8')
                    data_uri = f"data:image/png;base64,{img_base64}"
                    await ctx.progress.end()
                    return f"Successfully plotted '{formula}' using Desmos API. Image: {data_uri}"
                else:
                    ctx.warning("Desmos API call failed, falling back to local rendering.")
            except Exception as e:
                ctx.error(f"An exception occurred with Desmos API: {e}. Falling back to local rendering.")
    
        ctx.info("Using local rendering...")
        try:
            await ctx.progress.start(total=3, message="Starting local rendering...")
            
            await ctx.progress.report(1, 3, "Parsing formula...")
            expr = sympy.sympify(formula)
            x = sympy.symbols('x')
    
            await ctx.progress.report(2, 3, "Generating plot data...")
            plot_kwargs = {
                'show': False,
                'xlabel': 'x',
                'ylabel': 'y',
                'title': formula
            }
            if x_range:
                plot_kwargs['xlim'] = x_range
            if y_range:
                plot_kwargs['ylim'] = y_range
    
            p = sympy.plot(expr, (x, x_range[0], x_range[1]), **plot_kwargs)
    
            # Save the plot to a file
            desktop_path = os.path.join(os.path.expanduser('~'), 'Desktop')
            save_dir = os.path.join(desktop_path, 'Desmos-MCP')
            os.makedirs(save_dir, exist_ok=True)
            timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
            filename = f"plot_{timestamp}.png"
            p.save(os.path.join(save_dir, filename))
    
            await ctx.progress.report(3, 3, "Encoding image...")
            buf = io.BytesIO()
            p.save(buf)
            buf.seek(0)
            img_base64 = base64.b64encode(buf.read()).decode('utf-8')
            data_uri = f"data:image/png;base64,{img_base64}"
            
            await ctx.progress.end()
            return f"Successfully plotted '{formula}' using local rendering. Image: {data_uri}"
    
        except Exception as e:
            await ctx.progress.end()
            return f"Error plotting formula '{formula}' locally. Details: {e}"
  • Supporting method in DesmosAPIClient that performs the actual API call to plot the formula, used by the main handler when use_api is True.
    async def plot_formula(self, formula: str, x_range: List[float], y_range: List[float]) -> Optional[bytes]:
        """Requests a plot from the Desmos API and returns the image bytes."""
        headers = {
            "Authorization": f"Bearer {self.api_key}"
        }
        # 注意:此处的 payload 结构是基于通用 API 设计的推断,可能需要根据实际的 Desmos API 文档进行调整
        payload = {
            "formula": formula,
            "x_range": x_range,
            "y_range": y_range,
            "format": "png"
        }
        
        async with httpx.AsyncClient() as client:
            try:
                response = await client.post(f"{API_BASE_URL}/plot", headers=headers, json=payload, timeout=15.0)
                response.raise_for_status() # 如果状态码是 4xx 或 5xx,则引发异常
                return response.content
            except httpx.HTTPStatusError as e:
                print(f"Desmos API returned an error: {e.response.status_code} {e.response.text}")
                return None
            except httpx.RequestError as e:
                print(f"Error connecting to Desmos API: {e}")
                return None
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the fallback behavior between Desmos API and local rendering, which is useful context. However, it doesn't mention other behavioral traits like performance implications, error handling, authentication needs, rate limits, or output format details, leaving gaps for a tool with 4 parameters.

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 highly concise with two sentences that efficiently convey the core functionality and fallback mechanism. It's front-loaded with the main purpose and wastes no words, making it easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 4 parameters with 0% schema coverage and no annotations, the description is incomplete as it lacks parameter explanations and behavioral details. However, the presence of an output schema reduces the need to describe return values, and the purpose is clear, making it minimally adequate but with significant gaps.

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?

Schema description coverage is 0%, so the description must compensate, but it adds no information about parameters beyond the general context of plotting. It doesn't explain what 'formula', 'x_range', 'y_range', or 'use_api' mean or how they affect the plot, leaving all 4 parameters semantically unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generates a 2D plot' with a specific mathematical context. It distinguishes from siblings like 'analyze_formula' and 'validate_formula' by focusing on visualization rather than analysis or validation, though it doesn't explicitly contrast with 'plot_multiple_functions'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for plotting mathematical functions, with a fallback mechanism mentioned ('Tries Desmos API if available, otherwise falls back to local rendering'). However, it lacks explicit guidance on when to use this tool versus alternatives like 'plot_multiple_functions' or other siblings, and doesn't specify prerequisites or exclusions.

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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