get_cli_run_statusPoll the live progress of a test run: completed cases out of total, current pass/fail counts and elapsed time. Meant for watching a run that is still going; use get_test_run once it has finished. Requires project context.
| Name | Required | Description | Default |
|---|---|---|---|
| testRunId | Yes | Public Id (Guid) of the test run |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint: true already covers the safety profile. The description adds behavioral context by characterizing the tool as a 'poll' for 'live progress' and for 'watching a run that is still going,' implying repeated calls until completion. This goes beyond the annotation and gives useful operational insight.
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 two sentences and covers purpose, usage, and context without redundancy. It is well-structured and to the point, with no fluff 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 simple one-parameter schema and clear purpose, the description provides sufficient context: what the tool does, when to use it (vs. get_test_run), and a prerequisite (project context). No additional details are needed for a competent agent to invoke 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?
The schema already fully describes the single parameter (testRunId) as 'Public Id (Guid) of the test run.' The description adds semantic nuance by implying the run must be in progress for this tool to be appropriate (since it is meant for watching a run that is still going). This clarifies the expected state of the parameter.
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 specific action ('Poll the live progress of a test run') and its resource (a test run's progress). It also enumerates the exact data points returned (completed cases, pass/fail counts, elapsed time), making the purpose unambiguous.
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 contrasts this tool with its sibling get_test_run: 'Meant for watching a run that is still going; use get_test_run once it has finished.' This gives clear guidance on when to choose this tool over the alternative. It also mentions the requirement for project 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.
The tools are mostly distinct with clear descriptions. Some pairs like get_header_policies vs get_resolved_headers or get_environment_verification vs get_monitoring_sync_status could be slightly confusing, but the descriptions clarify scope and purpose.
All tools follow a consistent snake_case verb_noun pattern (get_, list_, create_, update_, manage_, etc.). Even the few bare verbs like 'search' and 'set_context' are consistent with the naming scheme.
With 165 tools, the server is extremely heavy. This far exceeds the 'too many' threshold of 25+, making it difficult for an agent to navigate and select the right tool efficiently.
The tool surface covers a very broad API lifecycle domain: specs, environments, test cases, monitors, mock servers, security, governance, documentation, and team management. Read and write operations are present across most areas, with no obvious missing core functionality.