SymKit

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assume

Set symbolic assumptions for variables to influence mathematical simplifications and calculations.

Instructions

    Set symbolic assumptions (affecting subsequent math() calculations)

    Assumptions are recorded in MathContext and passed to SymPy, and also written
    to the current session's multi-level assumption engine (session level).

    Args:
        variables: Mapping from variable to properties
                   e.g., {"x": "positive real", "n": "integer"}

    Returns:
        All current assumptions

    Example:
        assume({"x": "positive", "t": "real"})
        # Afterwards, math("simplify", "sqrt(x**2)") returns x instead of Abs(x)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "variables": "<variables>"
    },
    "name": "assume"
  }
}

Input Schema

Name	Required	Description	Default
`variables`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 8, 2026

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations, the description must fully disclose behavioral traits. It explains that assumptions are recorded in MathContext and the session engine, affecting math() calculations. However, it omits details on idempotency, overwriting behavior, error scenarios, or required permissions.

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 Args, Returns, and Example sections. It is reasonably concise, though it includes internal implementation details (MathContext, SymPy) that may not be essential for an AI agent.

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?

Given the tool's complexity and the existence of an output schema, the description covers the core functionality, side effects, and return value. It lacks mention of error handling or validation, but provides sufficient context for typical use.

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 a concrete example showing the expected format (e.g., {'x': 'positive real'}). This adds meaning beyond the schema's bare type definition, though it does not enumerate all possible property values.

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 that the tool sets symbolic assumptions affecting subsequent math() calculations. It uses a specific verb (set) and resource (assumptions), but lacks explicit differentiation from similar sibling tools like assume_for_step.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like assume_for_step, check_assumption_conflicts, or list_assumptions. It does not specify preconditions or scenarios where the tool is appropriate.

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