Aman-Amith-Shastry-scientific_computation_mcp

compute_eigen

Read-onlyIdempotent

    Computes the eigenvalues and right eigenvectors of a stored square matrix.

    Args:
        name (str): The name of the tensor to analyze.

    Returns:
        dict: A dictionary with keys:
            - 'eigenvalues': list of eigenvalues
            - 'eigenvectors': list of right eigenvectors, one per column of the result

    Raises:
        ValueError: If the tensor is not found or is not a square matrix.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes	Name of the stored square matrix to analyze.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 10, 2026

addedInput schema / properties / name / description

Added value: +"Name of the stored square matrix to analyze."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "compute_eigenDictOutput",
+  "type": "object"
+}

First observedJan 31, 2026

TDQS

A3.8/5.0

Behavior4/5

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

The description adds meaningful behavioral detail beyond the annotations: it specifies the return dict with eigenvalues and right eigenvectors, and explicitly documents ValueError for non-square or missing tensors. No contradiction with readOnlyHint/idempotentHint.

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 docstring is well-structured with Args, Returns, and Raises sectionshots, and the core behavior is front-loaded. It is slightly repetitive with the schema, but not enough to hurt clarity.

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

Completeness5/5

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

For a single-parameter read-only analysis tool, the description fully covers input requirements, return shape, and failure modes. The agent has everything needed to invoke and interpret the result.

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

Parameters3/5

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

The schema already describes the single parameter as the name of the stored square matrix. The docstring's Args section largely restates this, adding minimal new semantic value.

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 opens with 'Computes the eigenvalues and right eigenvectors of a stored square matrix' — a specific verb, clear target resource, and immediate distinction from sibling matrix tools like SVD, QR, or 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 given on when to use this tool instead of sibling tools, and no alternative tools are named. The square-matrix constraint is stated, but not developed into selection guidance 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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