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sablier-ai

Sablier MCP Server

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by sablier-ai

download_flow_paths

Read-only

Download generated flow paths as CSV using a flow job ID. Returns raw path data with columns for path index, day, and features for baseline or scenario generation jobs.

Instructions

Download all generated paths from a Flow generation job as CSV. Returns raw path data with columns: path_idx, day, then one column per feature. Works for both baseline and scenario generation jobs. Use the flow_job_id from generate_flow_paths, simulate_flow_scenario, or list_flow_baselines/list_flow_scenarios.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flow_job_idYesFlow generation job ID (from generate_flow_paths or simulate_flow_scenario)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint: true, so the safety profile is known. The description adds meaningful behavioral context beyond that: it returns raw path data with specific columns (path_idx, day, one per feature), is compatible with both baseline and scenario jobs, and provides a CSV output. This goes beyond the annotation's read-only flag without contradicting it.

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 three sentences, front-loaded with the main purpose, and each sentence earns its place. The second sentence details the return format, and the third gives crucial source guidance. No redundant words or repeated schema information.

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 1-parameter tool with an output schema and readOnly annotation, the description covers all necessary contexts: what it does, the exact output format, compatibility with baseline/scenario jobs, and where to obtain the required ID. Nothing important is missing for an agent to use it correctly.

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

Parameters4/5

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

The schema covers the only parameter (flow_job_id) with a description, so the baseline is 3. The tool description enriches this by expanding the list of valid source tools for the job ID to include list_flow_baselines/list_flow_scenarios, which is not present in the schema's parameter description. This is a useful addition for parameter selection.

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 starts with a specific verb 'Download' and clarifies the resource: 'all generated paths from a Flow generation job as CSV'. It explicitly names the output format and the data columns, and is clearly distinguishable from sibling tools like check_flow_job or get_flow_results by its focus on path download as CSV.

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?

The description clearly explains when to use the tool (to download generated paths) and provides specific prerequisite guidance by listing where the flow_job_id comes from (generate_flow_paths, simulate_flow_scenario, list_flow_baselines/list_flow_scenarios). It stops short of explicit when-not-to-use instructions, but the context is strong enough for an agent to decide correctly.

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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