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mcp-parigp

mcp-parigp

by daedalus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

matker

Compute the kernel of a matrix to find its null space. Identify vectors that map to zero under the linear transformation.

Instructions

Compute the kernel of a matrix.

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": {
      "m": "<m>"
    },
    "name": "matker"
  }
}

Input Schema

NameRequiredDescriptionDefault
mYesMatrix.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior1/5

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

With no annotations, the description must disclose behavioral traits, but it only states the purpose. It does not cover return format, side effects, authentication needs, or behavior for non-square matrices, leaving the agent uninformed.

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 a single sentence that is efficient and front-loaded, but it is too terse; additional detail could be included without becoming verbose. Still, it earns high marks for brevity.

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

Completeness1/5

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

Given the complexity of computing a kernel (nullspace) and the absence of an output schema, the description is critically incomplete. It omits essential information about return values, edge cases, and mathematical assumptions.

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?

Although schema coverage is 100%, the parameter description 'Matrix.' adds minimal meaning beyond the parameter name 'm'. It does not specify matrix type, dimensions, or constraints, failing to provide useful semantic nuance.

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 'Compute the kernel of a matrix' clearly identifies the tool's function with a specific verb (compute) and resource (kernel of a matrix), distinguishing it from sibling tools like matdet (determinant) and matinv (inverse).

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 is provided on when to use this tool versus alternatives, prerequisites (e.g., square matrix), or limitations (e.g., non-square matrices). The description lacks contextual advice for agent selection.

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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