Plan + execute a refactor that spans multiple repos owned by the caller. Wraps src/lib/multi-repo-refactor.ts. dry_run: true returns the plan only. Requires 'repo' scope.
| Name | Required | Description | Default |
|---|---|---|---|
| dry_run | No | When true, returns the plan and does NOT execute. | |
| description | Yes | Natural-language description | |
| repository_ids | No | Optional explicit repo IDs to scope the refactor to. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations list destructiveHint=false, but the description says it 'executes' a refactor, which may modify repos. The description does not disclose the actual side effects of execution (e.g., file modifications, commits), leaving ambiguity about behavioral traits. No additional context is provided beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise with four short sentences. It front-loads the core purpose and adds details like dry_run and scope requirement. Could be slightly more streamlined, but no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided. The description describes inputs and the dry_run behavior but does not explain what the execution returns (e.g., success message, diff). For a tool with this complexity, more completeness about return values would help.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%. The description adds minimal value; it restates the dry_run behavior and mentions the implementation file (multi-repo-refactor.ts), but the schema already adequately describes each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool plans and executes a refactor spanning multiple repos owned by the caller. It distinguishes itself from single-repo sibling tools like gluecron_write_file or gluecron_create_branch, which operate on a single repo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the dry_run option returns a plan only and requires 'repo' scope. It implies when to use (multi-repo refactor) but does not explicitly state when not to use it or suggest alternatives for single-repo refactors.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have near-identical purposes, such as `gluecron_read_file` and `gluecron_repo_read_file` (both read a file from a repo), and `gluecron_explain_repo` and `gluecron_repo_explain_codebase` (both return cached AI explanation). This creates ambiguity despite minor differences in description. While many tools are distinct, the overlapping pairs force an agent to choose between effectively equivalent operations, lowering disambiguation.
All tools use the `gluecron_` prefix followed by a verb_noun pattern (e.g., `acquire_lease`, `create_issue`, `merge_pr`). A few tools like `gluecron_ai_cost_summary` and `gluecron_repo_explain_codebase` deviate slightly but remain readable and predictable. Overall, the naming convention is largely consistent, making it easy to infer tool function from the name.
With 60 tools, the server far exceeds the 25-tool threshold for 'too many' per the guidelines. Although the server covers a broad developer platform (repository management, issues, PRs, workflows, AI features, etc.), the sheer number of tools makes navigation heavy and risks overwhelming both agents and users. A more focused set would improve coherence.
The tool set is remarkably thorough, covering nearly every lifecycle stage for repositories, issues, pull requests, workflows, branches, commits, and AI-assisted features (chat, test generation, release notes, refactoring, voice-to-PR). Essential CRUD operations are present, and advanced operations like leasing, sandbox provisioning, and multi-repo refactoring are included. There are no obvious gaps for the stated purpose of a developer platform.
