Skip to main content
Glama
kashrockapi

kashrock-mcp

Official
by kashrockapi

get_lines

Fetch consensus betting lines across sportsbooks, DFS teams, and prediction markets for any sport. Filter by event or market, and exclude started matches with live-only mode.

Instructions

Consensus mainlines across sportsbooks, DFS Teams, and prediction markets (Hobby+). live_only=true (default) drops started matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sportYes
marketNo
event_idNo
live_onlyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.5

TDQS

C2.9/5.0
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 does add one meaningful behavior: live_only=true (default) drops started matches, which explains an important default filter. However, it does not mention output format, authentication requirements, subscription limits (despite mentioning Hobby+), or any side effects. Partial transparency but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only two sentences and is front-loaded with the primary purpose before the parameter detail. Every word contributes; there is no filler. The only slight issue is that domain terms like 'mainlines' and 'Hobby+' are not expanded, but as a concise summary it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, 4 parameters (3 with default values that carry meaning), and no annotations, the description is too thin to fully support correct invocation. It does not explain what the response looks like, how to filter by market or event, or why 'Hobby+' is mentioned. An agent would need to guess at parameter formats and return structure, and might pick a sibling tool instead.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for explaining parameters. It only clarifies live_only, mapping 'true' to dropping started matches. It says nothing about sport (the required parameter), market, or event_id. The default-empty-string values for market and event_id are left unexplained. The description fails to compensate for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches consensus mainlines across multiple source types (sportsbooks, DFS teams, prediction markets), which is a specific verb+resource combination. It conveys the core purpose of getting aggregated lines. However, it does not distinguish itself from similar siblings like get_moneylines, get_best_lines, or get_live_odds, so it misses some differentiation credit.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus the many sibling tools such as get_moneylines, get_best_lines, or get_lines_history. The only contextual hint is the live_only parameter behavior, which is a parameter detail rather than a use-case selector. No alternative tools are named or conditions provided for choosing this one.

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