Skip to main content
Glama

run_check

Detect regressions in AI agent tests by comparing results against golden baselines. Identify PASSED, OUTPUT_CHANGED, TOOLS_CHANGED, or REGRESSION status to validate code changes.

Instructions

Check for regressions against the golden baseline. Returns a diff summary for each test: PASSED, OUTPUT_CHANGED, TOOLS_CHANGED, or REGRESSION. REGRESSION means the score dropped significantly — treat this as a blocking failure. TOOLS_CHANGED / OUTPUT_CHANGED are warnings: the agent's behavior shifted but may be intentional. Use this after any code change (prompt, model, tools) to confirm nothing broke. If you see a regression, show the diff to the user and offer to fix it before moving on. IMPORTANT: Automatically detect test_path by looking for a 'tests/evalview/' directory in the current project. If it exists, pass it as test_path. If the project has a custom test location, use that instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
testNoCheck only this specific test by name (optional, checks all by default)
test_pathNoPath to the test directory. Auto-detect: use 'tests/evalview/' if it exists, otherwise 'tests'.

Implementation Reference

  • The implementation of the 'run_check' tool handler in the MCP server, which executes `evalview check` as a subprocess.
    if name == "run_check":
        test_path = os.path.normpath(args.get("test_path", self.test_path))
        cmd = ["evalview", "check", test_path, "--json"]
        if args.get("test"):
            cmd += ["--test", args["test"]]
  • Registration and schema definition for the 'run_check' tool.
    {
        "name": "run_check",
        "description": (
            "Check for regressions against the golden baseline. "
            "Returns a diff summary for each test: PASSED, OUTPUT_CHANGED, TOOLS_CHANGED, or REGRESSION. "
            "REGRESSION means the score dropped significantly — treat this as a blocking failure. "
            "TOOLS_CHANGED / OUTPUT_CHANGED are warnings: the agent's behavior shifted but may be intentional. "
            "Use this after any code change (prompt, model, tools) to confirm nothing broke. "
            "If you see a regression, show the diff to the user and offer to fix it before moving on. "
            "IMPORTANT: Automatically detect test_path by looking for a 'tests/evalview/' "
            "directory in the current project. If it exists, pass it as test_path. "
            "If the project has a custom test location, use that instead."
        ),
        "inputSchema": {
            "type": "object",
            "properties": {
                "test": {
                    "type": "string",
                    "description": "Check only this specific test by name (optional, checks all by default)",
                },
                "test_path": {
                    "type": "string",
                    "description": (
                        "Path to the test directory. "
                        "Auto-detect: use 'tests/evalview/' if it exists, otherwise 'tests'."
                    ),
                },
            },
        },
    },
Behavior4/5

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

With no annotations, the description carries full burden and provides substantial behavioral context: it explains the return value meanings (PASSED, OUTPUT_CHANGED, etc.), severity levels (blocking vs. warnings), and auto-detection logic for test_path. However, it doesn't mention error handling, timeouts, or authentication needs.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded with core purpose and return values. Some sentences could be more concise (e.g., the auto-detection explanation is verbose), but overall it maintains focus with minimal waste.

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

Completeness4/5

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

For a tool with no annotations and no output schema, the description does well by explaining return value semantics and usage context. However, it lacks details on error conditions, performance characteristics, or example outputs, leaving some gaps in completeness.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents both parameters. The description adds context about auto-detection logic for test_path ('Automatically detect test_path by looking for a 'tests/evalview/' directory') but doesn't provide additional syntax or format details beyond what the schema provides.

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 the tool's purpose with specific verbs ('check for regressions', 'returns a diff summary') and resources ('golden baseline', 'each test'). It distinguishes from siblings by focusing on regression checking rather than test creation, listing, validation, or snapshot generation.

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

Usage Guidelines5/5

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

Explicit guidance is provided on when to use ('after any code change to confirm nothing broke') and how to interpret results (e.g., 'REGRESSION means blocking failure', 'TOOLS_CHANGED/OUTPUT_CHANGED are warnings'). It implicitly distinguishes from siblings by focusing on post-change validation rather than test generation or execution.

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/hidai25/eval-view'

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