Skip to main content
Glama

derivation_import_from_sympy

Import SymPy-MCP calculation results into NSForge as new steps, updating the current expression with assumptions and limitations for continued step-by-step derivation.

Instructions

    從 SymPy-MCP 導入結果回 NSForge

    ═══════════════════════════════════════════════════════════════════════
    🔄 HANDOFF 機制 - 把 SymPy-MCP 的結果帶回 NSForge 繼續!
    ═══════════════════════════════════════════════════════════════════════

    使用時機:
    - 在 SymPy-MCP 完成複雜計算後
    - 想要繼續使用 NSForge 的步進式記錄
    - 需要為 SymPy-MCP 的結果加入人類知識

    這個工具會:
    1. 將 SymPy-MCP 的結果記錄為新步驟
    2. 更新當前表達式
    3. 記錄使用的假設和限制

    Args:
        expression: SymPy-MCP 返回的表達式(字串格式)
        operation_performed: 執行了什麼操作(如 "Solved ODE")
        sympy_tool_used: 使用的 SymPy-MCP 工具名稱
        latex: LaTeX 格式(可選,會自動生成)
        notes: 額外說明
        assumptions_used: 使用的假設(從 SymPy-MCP 的 intro 來的)
        limitations: 這個結果的限制

    Returns:
        導入結果

    Example:
        # SymPy-MCP 解完 ODE 後
        derivation_import_from_sympy(
            expression="C*exp(k*t)",
            operation_performed="Solved first-order ODE",
            sympy_tool_used="dsolve_ode",
            notes="General solution with integration constant C",
            assumptions_used=["k is real positive", "t is real"],
            limitations=["Requires initial condition to determine C"]
        )
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latexNo
notesNo
expressionYes
session_idNo
limitationsNo
sympy_tool_usedYes
assumptions_usedNo
operation_performedYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool records a new step, updates the current expression, and records assumptions/limitations. It also mentions LaTeX is auto-generated if not provided. However, it does not mention session requirements or the precise return/error behavior, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with headings, bullets, an Args list, and an example. It front-loads the purpose and is scannable despite some decorative formatting and a slightly redundant handoff explanation. Efficient for the amount of detail conveyed.

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?

For a tool with 8 parameters and no annotations, the description covers usage timing, parameter semantics, side effects, and includes an example. It lacks details about session prerequisites and a precise return format, but overall it is largely complete for its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates with an Args section explaining expression, operation_performed, sympy_tool_used, latex (optional, auto-generated), notes, assumptions_used, and limitations. It misses session_id entirely, which is a schema parameter, though the schema's default may reduce the impact.

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 imports SymPy-MCP results back into NSForge, records them as a new step, updates the current expression, and logs assumptions/limitations. It distinguishes itself from sibling tools like derivation_export_for_sympy by explicitly naming the external source (SymPy-MCP) and the handoff purpose.

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 provides an explicit '使用時機' (when to use) section listing three concrete scenarios: after SymPy-MCP completes complex calculations, when wanting to continue with NSForge's step-by-step recording, and when needing to add human knowledge to results. It lacks explicit exclusions or alternatives, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/u9401066/nsforge-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server