List repos the workspace's GitHub connection can see (for import_project).
Each entry has full_name, default_branch, private, language, pushed_at. Pass
a `full_name` to import_project(repo_full_name) to connect it. Returns 409 if
GitHub isn't connected yet — call connect_github() first.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as a safe, read-only, idempotent operation. The description adds significant behavioral details: the return fields (full_name, default_branch, private, language, pushed_at), the 409 error condition when GitHub isn't connected, and the recommended action to resolve it. This goes well beyond the 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?
Three sentences, front-loaded with the action and purpose, followed by compact, high-value details about the response fields and error handling. No unnecessary words.
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?
For a simple list operation with no parameters and an output schema, the description covers the essentials: what the tool returns, how to use it, and what to do on failure. It's complete for an agent to use it correctly.
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?
There are zero parameters, so the baseline is 4. The description explains how to use the output (pass full_name to import_project), which adds helpful context for consumers of the returned data.
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 lists repos visible to the workspace's GitHub connection, with a specific purpose ('for import_project'). It distinguishes itself from siblings like import_project and connect_github by defining its exact scope and output fields.
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 provides clear usage context: it's for listing repos to be used with import_project, and it instructs to call connect_github() if a 409 is returned. However, it doesn't explicitly state alternatives or when not to use this tool, though the context makes it fairly obvious.
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.
Every tool targets a distinct resource and action duo, even within clusters like request handling or security reviews. The get_ vs run_ pairs are clearly separated, and descriptions explicitly contrast confusing alternatives such as archive_project vs delete_project.
The set overwhelmingly follows verb_noun snake_case (submit_request, list_projects, resolve_escalation). The one visible deviation is project_status, which breaks the get_/pattern, and signup is a single-word verb instead of sign_up.
37 tools is well above the 25+ threshold and spans auth, billing, project lifecycle, roadmap, escalations, product documents, and multiple review types. Most tools earn their place, but the surface is too large for one server and would be more coherent split into focused servers.
The domain coverage is broad: full project lifecycle, request intake/refinement, roadmap manipulation, escalation handling, product doc read/write, and security/legal review flows. Minor gaps exist, most notably no dedicated task-listing or task-update tool, but agents can work around these via project_status and list_escalations.