get_team_streaks
Retrieves active win, loss, draw, or clean sheet streaks for a specific team.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| teamId | Yes | The unique numeric ID. |
Retrieves active win, loss, draw, or clean sheet streaks for a specific team.
| Name | Required | Description | Default |
|---|---|---|---|
| teamId | Yes | The unique numeric ID. |
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 burden of behavioral disclosure. It states the operation is 'Retrieves' (read-only) and specifies 'active' streaks, but does not mention potential errors, response format, or any side effects. This is minimal but not misleading.
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, concise sentence that is front-loaded with the action and resource. No wasted words, making it easy to parse quickly.
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?
The tool has only one parameter and no output schema, so the description is mostly sufficient for an agent to select and invoke it. However, it does not describe the return structure (e.g., what a streak object contains), which is a minor gap given the lack of an output schema.
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% (teamId described as 'The unique numeric ID.'), so the schema already documents the parameter. The description adds no extra meaning beyond implying that the teamId identifies the team whose streaks are retrieved, matching the baseline.
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 uses a specific verb ('Retrieves') and resource ('active win, loss, draw, or clean sheet streaks for a specific team'), clearly stating what the tool does. It also implicitly distinguishes itself from sibling tools like get_match_form or get_team_last_x by focusing on streaks.
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 implies usage when needing active streaks for a specific team, but does not explicitly mention when to use this tool over alternatives or provide exclusions. It lacks guidance on scenarios where other team-related tools would be more appropriate.
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 (e.g., timelines vs stats vs lineups), but there are several closely related match-stat and season-fixture tools that could be confused, such as get_match_stats vs get_detailed_match_stats vs get_match_details_extended.
All tools use a consistent get_ prefix and snake_case, but naming conventions vary in structure (e.g., get_season_fixtures2 vs get_livescore_season_fixtures) and one tool includes an arbitrary number suffix ('fixtures2'), which is a minor deviation.
With 41 tools, the server is significantly overscoped for typical MCP use. Although the soccer domain is broad, the sheer number of highly granular tools makes it heavy and harder to navigate.
The tool set covers nearly every aspect of soccer data: fixtures, live scores, match details, standings, top scorers, injuries, referees, team streaks, and more. Minor gaps exist, such as no direct team search or player match performance, but the surface is otherwise comprehensive.