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sohumsuthar

ntopology-mcp

by sohumsuthar

set_literal_value

Set the value of a literal block in an nTop notebook, replacing scalars, file paths, vectors, points, booleans, or enums. Writes in place unless an output path is specified.

Instructions

Set the value of a literal block - a scalar, file path, vector, point, boolean or enum. Writes in place unless output is given.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesReplacement value, in the same shape the notebook already stores
outputNo
blockIdYes
notebookYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description alone must disclose behavioral traits. 'Writes in place unless output is given' is a useful but minimal hint; it does not explain what the output parameter does, whether the operation is destructive or reversible, whether special permissions are required, or what the return value is. For a mutation tool, this is a significant gap.

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 concise, using two sentences with no filler. The primary action and the key behavioral nuance ('writes in place unless output is given') are front-loaded, and every word contributes to the overall meaning.

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

Completeness2/5

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

Given that this is a mutation tool with no annotations, no output schema, and a sparse input schema, the description is under-specified. It fails to explain the output parameter, the exact effect of writing in place, any preconditions, or the response format. An agent would likely need additional information to invoke this tool confidently and correctly.

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

Parameters2/5

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

Schema description coverage is only 25% (only the 'value' parameter has a description). The description adds context for 'value' ('Replacement value, in the same shape the notebook already stores') but provides no semantic guidance for the 'output' parameter, which the description mentions only vaguely ('unless output is given') without clarifying its purpose or format. It also does not explain blockId or notebook beyond their names.

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 action (set) and the resource (a literal block), and enumerates the allowed value types (scalar, file path, vector, point, boolean or enum). It distinguishes from add_literal by use of 'set' (implying modification) and from set_input/set_output by focus on literal blocks, though it does not explicitly name alternatives.

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?

The description does not provide any explicit guidance on when to use this tool versus its siblings like add_literal (for creation) or set_input/set_output. The phrase 'Writes in place' hints that it works on an existing block, but there are no exclusions or alternative recommendations, leaving the agent to infer usage context.

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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