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sciple-idp

Sciple Platform MCP Server

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by sciple-idp

add_cell

Insert a markdown, shell, or HTTP cell into a runbook at a specified position, with optional execution targeting for Kubernetes, ECS, or EC2.

Instructions

Add a cell to a runbook.

Args: runbook_id: The runbook to add the cell to. cell_type: One of: 'markdown' (documentation), 'shell' (bash script), 'http' (HTTP request). content: The cell content — markdown text, bash script, or HTTP request body. order: Position in the notebook (0-indexed). Cells execute in ascending order. target_type: Execution target — 'k8s', 'ecs', or 'ec2'. Null for markdown cells. target_config: JSON string with target config: k8s: {"namespace": "production", "pod_selector": "app=api"} ecs: {"cluster_arn": "arn:aws:ecs:...", "task_definition": "my-task:5"} ec2: {"instance_id": "i-0abc123def456"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orderYes
contentYes
cell_typeYes
runbook_idYes
target_typeNo
target_configNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and adds useful behavioral context: cells execute in ascending order, target_type is null for markdown cells, and target_config examples show expected shapes. It does not discuss permissions, idempotency, or order-conflict handling, but the core behavior is transparent enough for safe invocation.

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 well structured: a one-sentence purpose followed by a compact Args list. Every parameter line adds value, and the JSON examples are informative rather than filler.

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

Completeness4/5

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

The description covers required parameters, cell types, execution order, and target configuration formats, which is enough to call the tool correctly. Minor gaps remain around whether target_type is mandatory for shell/http cells and how conflicting order values are resolved, but these do not block use.

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

Parameters5/5

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

All six parameters are fully explained despite 0% schema coverage: runbook_id, cell_type with allowed values, content, order semantics, target_type constraints, and target_config with concrete JSON examples. This completely compensates for the bare schema.

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?

Opens with a specific verb+resource pair: 'Add a cell to a runbook,' which clearly states the action and object. The add semantics distinguish it from siblings like update_cell, delete_cell, and reorder_cells, even without naming them.

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?

Provides clear context for when the tool is used: adding a cell to a runbook with position/order semantics and execution-target options. It does not explicitly name alternatives or exclusions, but the purpose and parameter detail make the usage context unmistakable.

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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