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hidai25

EvalView

by hidai25
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

list_tests

Retrieve all available golden baselines in an EvalView project to view test names, variant counts, and last update timestamps for regression testing.

Instructions

List all available golden baselines in this EvalView project. Shows test names, variant counts, and when each baseline was last updated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • evalview/mcp_server.py:429-430 (handler)
    The handler logic for 'list_tests', which maps the MCP tool call to the 'evalview golden list' CLI command.
    elif name == "list_tests":
    cmd = ["evalview", "golden", "list"]
  • evalview/mcp_server.py:147-157 (schema)
    The schema definition for the 'list_tests' tool, defining its name, description, and input parameters.
    {
    "name": "list_tests",
    "description": (
        "List all available golden baselines in this EvalView project. "
        "Shows test names, variant counts, and when each baseline was last updated."
    ),
    "inputSchema": {
        "type": "object",
        "properties": {},
    },
},
  • evalview/commands/mcp_cmd.py:279-280 (registration)
    A comment in the code acknowledging 'list_tests' as an exposed MCP tool.
    Exposes run_check, run_snapshot, and list_tests as MCP tools so you can
run regression checks inline without switching to a terminal.

Tool Definition Quality

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's read-only nature by using 'List' and 'Shows,' indicating it's a query operation without side effects. However, it lacks details on potential limitations like pagination, sorting, filtering options, or error conditions, which would be valuable for an agent to understand operational boundaries.

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?

The description is front-loaded with the core purpose in the first sentence and efficiently adds specific output details in the second sentence. Every word contributes value without repetition or fluff, making it highly concise and well-structured for quick comprehension by an agent.

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?

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is sufficiently complete. It clearly explains what the tool does and what information it returns. However, without an output schema, additional details on the exact return format (e.g., structure of the list, data types) would enhance completeness, but the current description is adequate for basic understanding.

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

Parameters4/5

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

The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter documentation in the description. The baseline score for 0 parameters is 4, as the description appropriately focuses on the tool's purpose and output without redundant parameter explanations, aligning with the schema's completeness.

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 specific action ('List all available golden baselines'), identifies the resource ('in this EvalView project'), and distinguishes it from siblings by focusing on baseline listing rather than creation, generation, running, or validation operations. It provides concrete details about what information is shown (test names, variant counts, last updated timestamps).

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

Usage Guidelines3/5

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

The description implies usage context by specifying 'available golden baselines' and 'EvalView project,' suggesting this tool should be used when needing an overview of existing baselines. However, it doesn't explicitly state when to use this tool versus alternatives (like 'run_snapshot' or 'validate_skill') or provide any exclusion criteria, leaving the agent to infer appropriate usage scenarios.

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

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