get_e2e_qa_run_detailsUse this when investigating a failed e2e QA run right after get_app_status reports e2e_tests.status='failed'. Returns the QA transcript, structured results, trace and replay URLs, and debugging keys_prefix.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | Target app id | |
| job_id | No | Optional QA job id to inspect | |
| qa_run_group_id | No | Optional QA run-group id from get_app_status |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond these hints by enumerating exactly what is returned: QA transcript, structured results, trace and replay URLs, and debugging keys_prefix. This informs the agent about the tool's outputs and its specific relevance to failed runs, without contradicting any 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 extremely concise: two sentences that front-load the use case and then list return artifacts. Every word earns its place, with no redundancy or irrelevant detail.
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 a simple read-only purpose, no output schema, and 3 well-documented parameters, the description adequately covers what the tool does, when to use it, and what it returns. It could mention what happens if the run is not found or if the run is not failed, but that is beyond the essential context provided. Overall, it is complete enough for an agent to select and invoke the tool 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?
Schema description coverage is 100%, with each parameter having a clear description (e.g., 'Target app id', 'Optional QA job id to inspect'). The description does not add new parameter-level details beyond what the schema provides, so the baseline score of 3 is appropriate. The usage line 'from get_app_status' in the schema already captures the relationship for qa_run_group_id.
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's purpose with a specific verb ('Returns...') and resource ('failed e2e QA run details'). It also distinguishes itself from the sibling tool get_app_status by specifying this is for investigating a failed run after status is reported, not for general status checks.
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 explicit context: 'Use this when investigating a failed e2e QA run right after get_app_status reports e2e_tests.status='failed'.' This clearly indicates when to use the tool and the prerequisite condition. It doesn't explicitly name alternatives or when-not-to-use scenarios, but the context is sufficiently clear.
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.
Most tools have distinct purposes, but the 5 secrets-related tools (create_secret_entry, delete_app_secrets, get_secret_entry_status, list_app_secrets, set_app_secrets) are numerous and could cause confusion. Similarly, deploy_app and apply_app_version both involve deployment but with different contexts. Overall, descriptions help differentiate, but some overlap remains.
The majority of tools follow verb_noun pattern (e.g., deploy_app, get_apps). However, the src_ prefix tools (src_glob, src_grep, src_read) deviate by using a noun prefix instead of a verb, creating a slight inconsistency. Otherwise, naming is clear and predictable.
With 21 tools, the server covers deployment, domain management, secrets, source inspection, and asset upload without being overly large. The count is well-scoped for a deployment platform, each tool serving a necessary role.
Core workflows (deploy, status, secrets, source inspection) are well-covered. However, there are minor gaps: no tool to list available templates or manage app metadata beyond deployment. The surface is mostly complete but could benefit from a few additional administrative tools.