Skip to main content
Glama

Execute Large Feature Plan Locally

execute_local_code_plan
Destructive

Orchestrate a local coding plan by breaking it into supervised subtasks, validating each step, and applying adaptive retries to deliver complete results.

Instructions

Compatibility full-result orchestrator. Supports local-supervised subtasks and adaptive fast-to-strong retry; prefer the compact orchestrator for enforced review behavior.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYes
tasksYes
contextNo
languageNo
workspaceYes
finalValidationNo
sharedConstraintsNo
sharedContextFilesNo
rollbackPlanOnFailureNo
Behavior3/5

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

Annotations already flag destructiveHint=true and readOnlyHint=false, so the safety profile is covered. The description adds behavioral context about full-result orchestration, local supervision, and adaptive fast-to-strong retry, but it does not disclose what destructive impact occurs (workspace edits, rollback behavior, failure consequences) beyond the annotation.

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 a single compact sentence with no filler, and the routing guidance is placed at the end. The phrases are dense and somewhat jargon-heavy, but the overall length and structure are appropriately concise for a tool with a rich schema.

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?

For a complex, destructive, 9-parameter orchestrator with no output schema, the description is too thin. It omits when to use this plan-level tool versus task-level siblings, how failures or retries behave, what 'full-result' means, and what the caller should expect after execution.

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% for top-level parameters, so the description must compensate by explaining goal, tasks, workspace, or the orchestrator's parameter relationships. It does not mention any parameter names or meanings, leaving all nine parameters effectively undocumented from the description's perspective.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The title 'Execute Large Feature Plan Locally' gives the primary action, and the description adds that it is an 'orchestrator' supporting subtasks and retries. However, the opening phrase 'Compatibility full-result orchestrator' is jargon-heavy and does not clearly state what the tool actually does from the description alone.

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 gives a clear routing signal by saying to 'prefer the compact orchestrator for enforced review behavior,' and it indicates this tool is the compatibility/full-result variant. It does not explicitly contrast with execute_local_code_task or delegate_code_task, but the compact-orchestrator exclusion is useful and concrete.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/gustavolbs/local-coder-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server