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mcp-numpy

mcp-numpy

by daedalus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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np_array

Convert a Python list into a NumPy array, optionally specifying the data type for numerical operations.

Instructions

Create a NumPy array from a list.

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": {
      "data": "<data>"
    },
    "name": "np_array"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataYesA Python list containing the array elements.
dtypeNoThe data type of the array (default: "float64"). Common values: "int32", "int64", "float32", "float64", "complex128".float64

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It only states the creation action without disclosing any behavioral traits such as input mutability, error cases, or performance characteristics. Important contextual details like whether the input list is modified or if the array is a copy are missing.

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

Conciseness5/5

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

The description is a single 7-word sentence that is efficient and front-loaded. Every word earns its place, with no wasted verbiage.

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 simple creation tool with an output schema (indicated by context), the description is adequately complete. It covers the core purpose and relies on the schema for parameter details. However, a brief note on return value (though covered by output schema) would improve completeness slightly.

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 input schema has 100% description coverage for both parameters ('data' and 'dtype'), so the description adds no extra semantic value beyond what the schema already provides. According to guidelines, high coverage justifies a baseline score of 3.

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 the verb 'Create' and the resource 'NumPy array from a list', distinguishing it from other array creation tools like np_zeros or np_arange that do not take a list as input. However, it does not explicitly contrast with siblings, missing the chance to reduce ambiguity.

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

Usage Guidelines3/5

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

The description implies usage when you have a Python list to convert to a NumPy array, but it provides no explicit guidance on when to use this tool versus alternatives (e.g., np_array vs. np_arange for ranges). No exclusions or conditions are mentioned.

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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