list_modelsList the user's trained models with pre-computed train/test stats.
| Name | Required | Description | Default |
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No arguments | |||
| Name | Required | Description | Default |
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No arguments | |||
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint=true. The description adds that the tool returns pre-computed train/test stats, which is a useful behavioral detail beyond annotations.
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?
A single clear sentence with no wasted words. Front-loaded and efficient.
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?
Given 0 parameters, rich annotations, and an output schema (present but not detailed here), the description fully covers the tool's purpose and output content.
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?
Input schema has no parameters (100% coverage). With 0 parameters, the description need not add param detail; baseline is 4.
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 it lists the user's trained models with pre-computed stats. It distinguishes from sibling tools like list_deployed (deployed models) and browse_community (community resources).
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?
The description implicitly indicates when to use it (to view trained models), but no explicit guidance on when not to use it or comparisons with alternatives like list_deployed or get_model_chart.
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.
Most tools have clearly distinct purposes, though browse_community, find_strategy, and one_shot (community_id) are related and could cause confusion if descriptions are not read carefully. Overall well-differentiated.
Overwhelmingly follows a verb_noun pattern (browse_community, find_strategy, generate_strategy, get_deploy_result, etc.). A few exceptions like 'one_shot' and 'top_up' break the pattern but are still clear and memorable.
13 tools cover the major functions of a trading strategy platform (discover, generate, deploy, monitor, account management). The count feels appropriate, though 'stream_test' is diagnotic and may not be needed in all contexts.
Covers core workflows well—browse, find, generate, deploy, and get results. However, lacks tools to update or stop a deployed model, and there's no way to edit an existing strategy, leaving some lifecycle gaps.