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junai-mcp

junai-mcp

by saajunaid
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

pipeline_reset

Reset a completed or failed pipeline to start a new run for the next feature, requiring explicit confirmation.

Instructions

Reset the current pipeline state and start a new pipeline run.

Identical to pipeline_init but semantically signals resetting an existing pipeline rather than creating a fresh one. Requires confirm=True. Use when a pipeline has closed and the user wants to start the next feature, or when explicitly restarting a failed/stale pipeline.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project": "<project>",
      "feature": "<feature>"
    },
    "name": "pipeline_reset"
  }
}

Input Schema

NameRequiredDescriptionDefault
typeNofeature
confirmNo
featureYes
projectYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.18

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description discloses that it resets state, requires confirm=True, and is identical to pipeline_init in behavior. However, it does not detail what gets destroyed (e.g., logs, intermediate data) or authorization prerequisites.

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?

Four sentences covering purpose, distinction from init, and usage scenarios. No fluff, but the third sentence could be merged with the second for tighter structure.

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 the tool's complexity (4 params, 2 required) and lack of annotation coverage, the description is adequate for the core purpose but falls short on parameter details. The output schema (not shown) may compensate, but description alone is incomplete.

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

Parameters1/5

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

Schema description coverage is 0%, and the description only mentions confirm=True requirement. It does not explain the meaning or purpose of project, feature, or type parameters beyond what the schema provides.

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?

Clearly states verb (reset) and resource (pipeline state), and explicitly distinguishes it from the sibling tool pipeline_init by noting it is semantically different.

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 scenarios for use (next feature after close, restarting failed/stale pipeline) and notes the requirement of confirm=True, but does not explicitly list when not to use or compare to other siblings beyond pipeline_init.

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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