List the user's saved programs at the same summary level as LiftTrack's program cards: name, status, total weeks, current week when running, and days per week. Status is not_started, active, paused, or complete. Internal program lifecycle ids are never returned.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| programs | Yes |
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 annotations already declare this a safe, read-only, idempotent operation, so the bar is lower. The description adds meaningful behavior beyond the annotations: the guarantee that internal lifecycle ids are never returned, and the specific status vocabulary. This helps the agent set expectations about the response without overpromising.
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?
Three short sentences with no filler. The primary action and output fields are front-loaded, the status enum is given in a compact list, and the critical exclusion (no internal IDs) is stated directly. Every sentence contributes valuable information.
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?
For a simple read-only list tool with zero parameters, an output schema, and safety annotations, the description is complete. It specifies the user scope, result granularity, included fields, status values, and a key constraint. Nothing an agent needs to decide whether to call this or interpret its result is missing.
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?
With zero parameters and 100% schema coverage, there is nothing for the description to explain about parameter meanings. The baseline for 0-parameter tools is 4, and the description goes further by clarifying the output semantics (the fields and statuses returned), which is useful context even though it is not strictly parameter-related.
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 states a specific verb ('List'), a clear resource ('the user's saved programs'), and a precise scope: the summary level of LiftTrack's program cards. It enumerates the exact fields returned (name, status, total weeks, current week, days per week), which fully distinguishes it from the sibling get_program_detail. This is a model of purpose clarity.
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 clearly implies this tool is for summary-level program listing, not detail retrieval, and explicitly notes that internal lifecycle ids are never returned. This gives the agent enough context to know that get_program_detail is the alternative when more than the card-level summary is needed. However, it does not explicitly name a sibling or state 'use this when...' so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
