Generate an AI commit-message-style description for a diff. Uses src/lib/ai-commit-message.ts under the hood; gracefully degrades to a heuristic when ANTHROPIC_API_KEY is missing. Returns {subject, body}.
| Name | Required | Description | Default |
|---|---|---|---|
| diff | Yes | Unified-diff body | |
| style | No | 'conventional' (default) or 'plain' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds value by disclosing the internal module ('src/lib/ai-commit-message.ts') and graceful degradation to heuristic when ANTHROPIC_API_KEY is missing. This goes beyond annotations without contradicting them.
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 highly concise with two sentences: first states the purpose, second adds implementation detail and return type. Every sentence serves a function with no redundancy.
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 no output schema, the description includes the return shape ({subject, body}). Parameter semantics are covered in the schema. The behavior (AI vs heuristic) is explained. Minor gaps (e.g., diff size limits) are not critical for a simple tool.
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 description coverage is 100% with both parameters ('diff' as 'Unified-diff body' and 'style' with default 'conventional' or 'plain'). The description does not add new parameter-level meaning but complements with return format and fallback behavior, resulting in a baseline score.
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 generates an AI commit-message-style description for a diff, specifying the verb ('generate') and resource ('commit-message-style description for a diff'). It differentiates from siblings like 'gluecron_generate_commit_message' by focusing on PR descriptions from diffs and mentioning the internal implementation.
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 implies usage for diff-to-PR-description conversion but lacks explicit when-to-use guidance or comparisons with alternatives. It does not mention when not to use it or suggest siblings like 'gluecron_generate_commit_message' for other contexts, providing only implicit context.
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.
