Aman-Amith-Shastry-scientific_computation_mcp

view_tensor

Read-onlyIdempotent

Returns an immutable view of a previously stored NumPy tensor from the in-memory tensor store.

Args:
    name (str): The name of the tensor as stored in the in-store dictionary

Returns:
    Tensor: The stored tensor as nested lists.

Raises:
    ValueError: If the tensor name is not found in the store.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes	Name of the stored tensor to read back.

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 read back."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "items": {
+            "type": "number"
+          },
+          "type": "array"
+        },
+        {
+          "items": {
+            "items": {
+              "type": "number"
+            },
+            "type": "array"
+          },
+          "type": "array"
+        },
+        {
+          "items": {
+            "items": {
+              "items": {
+                "type": "number"
+              },
+              "type": "array"
+            },
+            "type": "array"
+          },
+          "type": "array"
+        }
+      ],
+      "title": "Result"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "view_tensorOutput",
+  "type": "object"
+}

First observedJan 31, 2026

TDQS

A4/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses important runtime behavior: the returned value is an immutable view, it is returned as nested lists, and it raises ValueError when the name is missing. This gives the agent a clear model of what happens on both success and failure.

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 well-structured with Args, Returns, and Raises sections, making it easy to parse. It is concise, though there is slight redundancy between 'previously stored' and 'as stored in the in-store dictionary'.

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 tool with full annotations and an output schema, the description covers the essential details: what it returns, where the data lives, and the failure mode. Nothing critical is missing for an agent to invoke it correctly.

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 coverage is 100%, so the parameter is already well-documented by the schema. The description adds minor context by clarifying that the name is used as the key 'as stored in the in-store dictionary', but this does not substantially change an agent's understanding beyond the schema.

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 identifies the verb ('Returns'), resource ('previously stored NumPy tensor'), and source ('in-memory tensor store'). It distinguishes this as a read-only retrieval tool relative to mutation siblings like create_tensor and delete_tensor, though it doesn't explicitly name those alternatives.

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 phrase 'previously stored' implies the tool should be used after a tensor has been created, and the ValueError note indicates a precondition that the name must exist. However, there is no explicit statement about when to choose this tool over siblings or when not to use it.

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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