Open a draft pull request. Same payload as gluecron_create_pr but forces is_draft=true. Useful for AI-in-progress PRs that shouldn't run mergeability checks yet.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | ||
| repo | Yes | ||
| owner | Yes | ||
| title | Yes | ||
| base_branch | No | ||
| head_branch | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide `readOnlyHint: false` and `destructiveHint: false`, indicating mutability but no destruction. The description adds the key behavior that it forces `is_draft=true`, which is beyond what annotations convey. No contradictions.
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?
Two sentences, no filler, delivers core message efficiently. Every sentence adds value: first defines purpose, second gives usage context.
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?
Given the tool is a specialized variant, the description adequately explains its unique feature (draft status) and use case. However, it assumes knowledge of the sibling tool's payload, and lacks any parameter documentation, which could hinder standalone usability for an agent unfamiliar with `gluecron_create_pr`.
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 has 6 parameters with 0% description coverage. The description does not explain any parameters or their semantics. It only states 'Same payload as gluecron_create_pr', which is insufficient for an agent to know required fields or formats without consulting another tool's definition.
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 opens a draft pull request and distinguishes it from sibling `gluecron_create_pr` by noting the forced draft status. The verb 'open' and resource 'draft pull request' are explicit.
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 explicitly states when to use this tool: 'Useful for AI-in-progress PRs that shouldn't run mergeability checks yet.' This clearly implies when not to use it, and references the alternative `gluecron_create_pr` indirectly.
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.
