Interpret a free-form voice transcript and either ship it as a spec or create an issue (caller picks via as). Wraps src/lib/voice-to-pr.ts. Requires 'repo' scope.
| Name | Required | Description | Default |
|---|---|---|---|
| as | No | 'spec' or 'issue' (default: auto via interpretVoiceTranscript) | |
| repo | Yes | ||
| owner | Yes | ||
| transcript | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and destructiveHint=false, so the description's mention of creating issues/specs aligns but adds little beyond that. It notes a scope requirement ('repo') and internal file, but no details on side effects, rate limits, or failure modes.
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 three sentences, front-loading the main action, then providing internal context, and finally a requirement. Every sentence adds value with no redundancy or extraneous information.
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 has 4 parameters (3 required), no output schema, and many sibling tools, the description omits essential details like what the tool returns, error handling, or explanations for 'owner', 'repo', and 'transcript'. The agent lacks enough context to use it correctly without additional documentation.
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?
Only one of four parameters ('as') has a schema description, and the description echoes that it picks 'spec' or 'issue'. The essential parameters 'owner', 'repo', and 'transcript' are not explained, leaving a significant gap for the agent despite low schema coverage (25%).
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 interprets a free-form voice transcript and either ships a spec or creates an issue, using the 'as' parameter to choose. This distinguishes it from sibling tools like gluecron_ship_spec and gluecron_create_issue, which don't involve voice interpretation.
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 it requires 'repo' scope and wraps an internal module, but does not explicitly state when to use this tool versus alternatives like gluecron_create_issue or gluecron_ship_spec. The context of 'voice transcript' implies a specific use case, but no comparative guidance is provided.
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.
