bet-checker
Server Details
Grade any bet against thousands of play-by-play game simulations: win probability, odds, edge.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 3 of 3 tools scored. Lowest: 3.1/5.
Each tool has a clear, distinct purpose: check_bet evaluates a specific bet, get_slate provides slate availability, and get_usage shows remaining checks. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern using snake_case: check_bet, get_slate, get_usage. This makes the set predictable and easy to navigate.
With only 3 tools, the server is tightly scoped, which is appropriate for a focused betting utility. The count is slightly low but reasonable given the narrow domain of checking bets and slate.
The set covers the core workflow: checking slate, grading a bet, and monitoring usage. Minor gaps exist, such as not having a tool to list supported sports or markets, but these are addressed via status messages in check_bet.
Available Tools
3 toolscheck_betAInspect
Grade a sports bet against NegativeEV's game simulations. Input the bet as plain text (teams/players, market, line, odds). Returns each leg's simulated probability vs the implied odds and the resulting edge, or a status explaining why the bet can't be graded (no_slate, unsupported_market, unsupported_sport, no_bet_found). Covers MLB and WNBA; any other sport returns unsupported_sport. Games that already started still grade, against the simulations from before first pitch (a pre-game projection, flagged as such); spends one of the caller's checks when the bet fully grades.
| Name | Required | Description | Default |
|---|---|---|---|
| bet | Yes | The bet text, e.g. 'Aaron Judge over 1.5 total bases -120' |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses critical behavior beyond annotations: consumes caller's checks when bet fully grades, uses pre-game projections for started games. Annotations (readOnlyHint=false, openWorldHint=true) are consistent with description.
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?
Description is moderately sized and informative, with each sentence adding value. Front-loaded with purpose. Slightly verbose but not redundant, earning a 4.
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?
Comprehensive coverage of tool behavior: input format, supported sports, handling of started games, resource consumption, and possible return statuses. No output schema but description sufficiently explains return values.
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?
Schema coverage is 100% with description for 'bet' parameter. Description adds meaning: specifies input format as plain text with examples of content (teams, market, line, odds), enhancing schema.
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?
Clearly states the tool grades a sports bet using simulations, with explicit verb 'Grade' and resource 'sports bet'. Distinguishes from siblings (get_slate, get_usage) by specific function.
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?
Provides clear usage context: input as plain text, covers MLB/WNBA, behavior for started games, and possible status codes. Does not explicitly name alternatives but sibling tools are distinct, so no confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_slateARead-onlyInspect
Whether there is a checkable slate right now (at least one simulated, not-yet-started game) in each live sport: MLB and WNBA. Pass sport to ask about one; omit it for all of them. Free — spends no check.
| Name | Required | Description | Default |
|---|---|---|---|
| sport | No | One of: MLB, WNBA. Omit to get every live sport. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, indicating safe read operation. The description adds 'Free — spends no check', reinforcing non-destructive behavior and cost implications, which is valuable 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?
Two concise sentences that front-load purpose and usage. Every word is necessary; no redundancy or fluff.
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?
While the description covers purpose and parameter usage, it lacks details about the return format or structure. Given no output schema, this might leave the agent uncertain about the response type.
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?
Schema description coverage is 100% and already explains the parameter. The description repeats the same info, adding no new semantic value beyond what the schema provides.
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 the tool checks for a checkable slate in MLB and WNBA sports, using a specific verb-resource pair. However, it does not explicitly distinguish from sibling tools like check_bet or get_usage.
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 explains when to pass the 'sport' parameter and when to omit it, providing clear usage context. It does not include exclusions or alternatives but is sufficient for a simple tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_usageBRead-onlyInspect
How many bet checks the caller has left and when the next one frees up — the daily allowance for anonymous callers, the hourly pace window for accounts. Free — spends no check.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint true. The description adds context about daily allowance and hourly pace, enhancing transparency beyond annotations. However, it does not fully disclose output format or edge cases.
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 a single sentence with a dash and fragment. It is somewhat concise but lacks clear structure and includes a cryptic note ('Free — spends no check'), reducing clarity.
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?
With no output schema, the description should explain the return format. It mentions 'how many bet checks left' and 'when next frees up' but does not provide enough detail for an agent to interpret the response correctly.
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?
No parameters exist, so schema provides full coverage. The description adds meaning by explaining what the tool returns, earning a baseline 4 for zero parameters.
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 it returns usage information about bet checks left and next free time, distinguishing between anonymous and account users. However, the phrase 'Free — spends no check' is cryptic and could be clarified.
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?
No guidance on when to use this tool versus siblings (check_bet, get_slate). The description only explains what it does, not when to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!