get_my_bets
Your paper bets, newest first.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes |
Your paper bets, newest first.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | 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?
With no annotations, the description carries the behavioral disclosure burden. It discloses the scope ('your paper bets') and ordering ('newest first'), but does not mention response shape, pagination, error behavior, or whether it is strictly read-only.
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?
The description is extremely concise at six words and contains no filler. It front-loads the useful ordering behavior, though it may be too minimalist for full completeness.
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 single-parameter list getter with no output schema, the description covers the core idea and sort order but leaves out agent_id semantics and response structure. It is minimally adequate but not richly complete.
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?
The schema provides only the parameter name 'agent_id' with type string and 0% description coverage. The description gives only an indirect link via 'your', which may help infer that the parameter identifies the betting agent, but it does not explicitly explain how to use agent_id.
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 identifies the resource as the user's paper bets and specifies ordering as newest first. It does not explicitly state the verb 'get', relying on the tool name, and does not fully distinguish itself from get_bet, but the plural 'bets' and 'my' make the purpose reasonably clear.
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 gives no explicit guidance on when to use this tool versus siblings such as get_bet or get_value_bets. There are no exclusions, alternatives, or contextual conditions provided.
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 target clearly distinct resources (predictions, bets, balance, leaderboard, record). However, get_predictions and get_value_bets are quite similar in scope—one being the unfiltered version of the other—so an agent could misselect without clearly reading paywalled requirements.
The get_ prefix is used for most retrieval operations, with place_bet, submit_prediction, fetch, and search as notable exceptions. The verb-noun structure is otherwise consistent, so the deviations are minor.
The 13-tool surface is proportional to the server's purpose of enabling paper betting, prediction retrieval, and performance tracking. Each tool maps to a distinct action within those workflows without feeling redundant.
The domain covers the full lifecycle: searching/fetching predictions, submitting and valuing predictions, placing and viewing bets, checking balance, and viewing performance via track record, CLV scores, and leaderboard. No obvious gaps prevent core workflows.