Skip to main content
Glama

save_test_result

Idempotent

Record test outcome (pass or fail) for a project, linked to the active task. Updates existing memory for the same test suite.

Instructions

Save a test result memory for a project, linked to the current active task.

Call this after running tests — pass or fail — to record the outcome. Saves a memory with category='test_result' and links it to the most recent active task. Re-running with the same test_suite name updates the existing memory (living document).

USAGE:

  • After a passing test run: save_test_result({ project_name, passed: true, test_suite })

  • After a failing run: save_test_result({ ..., passed: false, failure_details: "..." })

RETURNS:

  • memory_id — UUID of the saved test result memory

  • status — "PASSED" or "FAILED"

  • linked_task — the active task this result is associated with (if any)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_nameYesThe project name (e.g. "purmemo")
passedYesWhether the test suite passed
test_suiteYesName of the test suite (e.g. "get_next_task e2e")
failure_detailsNoDetails about what failed — only include when passed=false
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses idempotent behavior: 'Re-running with the same test_suite name updates the existing memory (living document).' Also explains linking to active task and return values. Adds significant value beyond annotations which only hint at idempotency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, well-structured with clear sections (purpose, usage, returns). Every sentence adds value without redundancy. Front-loaded with core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 4 parameters, no output schema, but description thoroughly covers inputs, behavior (update on re-run), linking, and return values. Complete for a straightforward save tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 4 parameters are described in schema (100% coverage), but the description adds extra context: failure_details is only for passed=false. Usage examples show parameter combinations, enhancing schema clarity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Save a test result memory for a project, linked to the current active task,' using specific verb and resource. It uniquely identifies the tool's purpose among siblings like 'save_artifact' or 'save_investigation_result'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Call this after running tests — pass or fail — to record the outcome' and provides examples for both passing and failing cases. Does not explicitly exclude other use cases or mention alternatives, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/purmemo-ai/purmemo-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server