Aman-Amith-Shastry-scientific_computation_mcp

delete_tensor

DestructiveIdempotent

Deletes a tensor from the in-memory tensor store.

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

Returns:
    str: Confirmation that the tensor was removed.

Raises:
    ValueError: If the tensor name is not found in the store or if an error occurs during deletion.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes	Name of the stored tensor to remove from the store.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

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 tensor to remove from the store."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "title": "Result",
+      "type": "string"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "delete_tensorOutput",
+  "type": "object"
+}

First observedJan 31, 2026

TDQS

B3.4/5.0

Behavior1/5

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

Annotation Contradiction: The description says ValueError is raised if the tensor name is not found, implying a second delete of the same name fails. This contradicts the idempotentHint=true annotation, which claims repeated identical requests produce the same outcome. Additionally, the description adds little beyond annotations: the destructive behavior is already declared, and 'if an error occurs during deletion' is vague.

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 compact and front-loaded with the core purpose. The structured Args/Returns/Raises format is easy to parse. Minor redundancy exists because the Args section repeats schema information, and the 'or if an error occurs during deletion' clause is imprecise, but overall it is efficient.

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

Completeness3/5

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

For a simple single-parameter deletion tool, the description covers the action, parameter, return value, and error condition. However, the idempotency contradiction creates meaningful ambiguity about repeated calls, and the description does not clarify irreversibility beyond the annotation. The output schema and annotations fill many gaps, but the contradiction undermines completeness.

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?

Schema description coverage is 100%, so the schema fully documents the required 'name' parameter. The description repeats the parameter meaning but adds no extra format, constraints, or edge-case details beyond what the schema provides. Baseline 3 applies.

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 states a specific verb and resource: 'Deletes a tensor from the in-memory tensor store.' This clearly distinguishes it from sibling tools like create_tensor, view_tensor, and matrix operations. It also names the target store, leaving no ambiguity about what the operation affects.

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

Usage Guidelines4/5

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

The context is clear: use this tool to remove a named tensor from the in-memory store. However, it does not explicitly mention when not to use it or name alternatives such as create_tensor or view_tensor. The usage context is strong but exclusions are absent.

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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