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edit_cell

Modify code in existing JupyterLab cells and re-execute them to update outputs within remote HPC sessions.

Instructions

Edit an existing cell, re-execute it, and update outputs.

Args: session_id: Session identifier. cell_index: Cell index (supports negative indexing). code: New code for the cell.

Returns: Formatted output string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYes
cell_indexYes
codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden but provides minimal behavioral context. It mentions that editing triggers re-execution and output updates, but doesn't cover important aspects like error handling, permission requirements, whether changes are reversible, or side effects on other cells/sessions.

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 appropriately sized and front-loaded with the core functionality. The Args/Returns sections are structured but could be more integrated. Every sentence adds value, though the formatting is slightly verbose.

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?

Given 3 parameters with 0% schema coverage and no annotations, the description provides basic parameter semantics and mentions the return format. However, for a mutation tool that edits and executes code, it lacks sufficient context about behavioral implications, error conditions, and integration with sibling tools. The output schema exists, reducing the need to explain return values.

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 0%, so the description must compensate. It provides basic semantic meaning for all three parameters (session_id as identifier, cell_index with negative indexing support, code as new content), but lacks details about format constraints, valid ranges, or examples. This meets the baseline for 0% coverage.

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 tool's purpose with specific verbs ('edit', 're-execute', 'update') and identifies the resource ('existing cell'). It distinguishes this from siblings like 'add_markdown' (creation) and 'execute_code' (execution only), but doesn't explicitly contrast with all siblings like session management tools.

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?

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing an active session), exclusions, or comparisons to similar tools like 'execute_code' for execution without editing.

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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