get_test_resultsGet the test run history for a test suite. Shows past executions with their status and pass/fail counts. Requires project context.
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | Number of runs to skip (for pagination, default 0) | |
| take | No | Number of runs to return (default 20, max 100) | |
| testSuiteId | Yes | Public ID (GUID) of the test suite |
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 description is consistent with the readOnlyHint annotation, stating it 'Shows past executions,' implying no side effects. However, it does not add significant behavioral details beyond this, such as pagination behavior of skip/take parameters or error handling. It partially discloses output contents but not operational aspects.
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 concise: two sentences that front-load the main purpose and then add detail on what is shown. It avoids unnecessary verbosity and is well-structured.
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?
The description provides enough context for a basic read operation, including the resource type and output summary. It could mention the output format more explicitly or edge cases, but given the simplicity and lack of an output schema, it is largely complete.
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?
All parameters are described in the schema with clear details (testSuiteId as GUID, skip and take with defaults and max). The description itself does not add extra meaning beyond what is already in the schema, so it stays at the baseline for high schema coverage.
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 function: 'Get the test run history for a test suite' and specifies the resource and scope (test suite). It further explains what is shown (past executions with status and pass/fail counts), making it distinct from other get_* tools.
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?
No explicit guidance on when to use this tool versus alternatives. Sibling tools like get_test_result, get_test_run, and list_test_runs exist, but the description does not differentiate when to choose one over the other. The only prerequisite mentioned is 'Requires project context,' which is vague.
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.