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mcp-sympy

by daedalus
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Python
Remote

sympy_matrix_rank

Compute the rank of a symbolic matrix to determine its linear independence and dimensionality.

Instructions

Matrix rank.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
matrixYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • src/mcp_sympy/tools.py:2420-2423 (handler)
    The handler function for the sympy_matrix_rank tool. Parses a string matrix expression via _sympify() and calls .rank() on the resulting SymPy matrix, returning the result as a string.
    @mcp.tool()
def sympy_matrix_rank(matrix: str) -> str:
    """Matrix rank."""
    return str(_sympify(matrix).rank())
  • src/mcp_sympy/tools.py:114-116 (helper)
    Helper function that converts string input to SymPy objects, used by the handler to parse the matrix argument.
    def _sympify(expr: str) -> sympy.Basic:
    """Convert string expression to SymPy object."""
    return sympy.sympify(expr)
  • src/mcp_sympy/tools.py:2420-2420 (registration)
    Registration via the @mcp.tool() decorator on the FastMCP instance 'mcp' (defined at line 119).
    @mcp.tool()
  • src/mcp_sympy/tools.py:119-119 (registration)
    The FastMCP instance 'mcp' that registers all tools including sympy_matrix_rank via the @mcp.tool() decorator.
    mcp = fastmcp.FastMCP("mcp-sympy")

Tool Definition Quality

C2.3/5.0
Behavior1/5

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

No annotations are provided, and the description only states 'Matrix rank' without disclosing any behavioral traits such as input validation, error handling, return type details, or whether symbolic matrices are supported. The output schema exists but is not described.

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

Conciseness2/5

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

While extremely concise at two words, the description omits critical information. It sacrifices clarity for brevity, resulting in under-specification rather than effective conciseness.

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

Completeness2/5

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

Given the tool's simplicity (one parameter, output schema), the description should provide sufficient context for correct usage. However, it lacks behavioral guidance, parameter semantics, and usage context, making it incomplete for an AI agent.

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?

The single parameter 'matrix' is of type string with no description in the schema. The description adds no additional meaning, leaving the agent to infer that it expects a string representation of a matrix. Schema coverage is 0%, and the description does not compensate.

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 'Matrix rank' clearly indicates the tool computes the rank of a matrix. It is specific enough to distinguish from sibling matrix tools like determinant or inverse, though it lacks explicit verb or differentiation.

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?

No guidance on when to use this tool versus alternatives, no prerequisites, and no examples. The description provides no context for typical use cases.

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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