Aman-Amith-Shastry-scientific_computation_mcp

add_matrices

Read-onlyIdempotent

    Adds two stored tensors element-wise, computing name_a + name_b.

    Args:
        name_a (str): The name of the first tensor.
        name_b (str): The name of the second tensor.

    Returns:
        Tensor: The result of element-wise addition.

    Raises:
        ValueError: If the tensor names are not found or shapes are incompatible.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name_a`	Yes	Name of the first stored tensor.
`name_b`	Yes	Name of the second stored tensor.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedSep 10, 2026

addedInput schema / properties / name_a / description
```
Added value: +"Name of the first stored tensor."
```
addedInput schema / properties / name_b / description
```
Added value: +"Name of the second stored tensor."
```

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": "add_matricesOutput",
+  "type": "object"
+}

First observedJan 31, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already establish readOnly, idempotent, non-destructive behavior. The description adds context beyond this by stating it operates on stored tensors by name and by documenting ValueError conditions for missing names or incompatible shapes. It does not contradict annotations.

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 compact and well-structured with Args, Returns, and Raises sections. The phrase 'computing name_a + name_b' is mildly redundant after 'element-wise,' but the overall structure is clean and front-loaded.

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 simple two-parameter, read-only tensor operation with an output schema and full annotations, the description covers the operation, error conditions, and result type. Nothing an agent needs to call it correctly is missing.

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%, with both parameters described as 'name of the first/second stored tensor.' The description's Args section repeats essentially the same information and adds no new semantic detail, so the baseline score of 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 ('Adds'), a resource ('stored tensors'), and clarifies the operation is element-wise with 'computing name_a + name_b.' This distinguishes it from matrix multiplication siblings like multiply_matrices, and the operation is unambiguous against subtract/multiply siblings.

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—call this when you need element-wise addition of two stored tensors—but it provides no explicit when-to-use vs alternatives, no exclusions, and no mention of sibling tools like subtract_matrices or multiply_matrices. The Raises section hints at constraints but doesn't help an agent select among operations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Aman-Amith-Shastry-scientific_computation_mcp tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.