get_test_resultGet one test case's result inside a run: status code, timing, the response body and headers, every assertion outcome, extracted variables and any script output. This is the read to make when a run failed and you need to know why. Get the id from get_test_results. Requires project context.
| Name | Required | Description | Default |
|---|---|---|---|
| testResultId | Yes | Public Id (Guid) of the test result |
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 readOnlyHint annotation establishes the read-only behavior, and the description reinforces this by calling it 'the read to make' and using a non-mutating verb. No side effects are claimed or contradicted.
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 compact and information-dense, with no redundant phrases. It front-loads the resource and purpose, then lists useful return contents and usage context in a few sentences.
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?
Despite lacking an output schema, the description enumerates the key fields returned and provides both usage conditions and prerequisite context. An agent has enough information to decide when and how to call this tool.
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 single parameter testResultId is fully described in the schema as a Public Id (Guid), and the description adds practical guidance on obtaining it from get_test_results. This exceeds the baseline 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?
States precisely that it retrieves one test case's result within a run and enumerates the returned data (status code, timing, response body/headers, assertions, extracted variables, script output). Clearly distinguishes from sibling tools like get_test_results and get_test_run.
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?
Explicitly indicates when to use it ('when a run failed and you need to know why'), where to obtain the required id ('from get_test_results'), and that project context is required. This gives the agent clear call-time guidance.
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.