Return driver list for a specific F1 session.
Args: session_key: OpenF1 session identifier.
Returns: data.drivers: list of driver objects with driver_number, full_name, team. meta.source: adapter that served the data.
| Name | Required | Description | Default |
|---|---|---|---|
| session_key | Yes | OpenF1 session identifier. |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| meta | No | ||
| error | No |
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?
Annotations already carry the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description adds modest context by documenting the response shape (data.drivers with specific fields, meta.source), but since an output schema exists, this adds limited value beyond the structured data. No contradiction with 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?
The description is compact and well-structured with clear Args/Returns sections, and the purpose is front-loaded. One minor deduction: the Args section duplicates the schema exactly, which adds no value beyond the structured field.
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 one-parameter, read-only tool with full schema coverage, an output schema, and complete safety annotations, the description is nearly sufficient. The only gap is routing guidance—how to acquire the session_key and when to prefer this tool over sibling tools—which is a minor omission given the simplicity.
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%, so the schema already fully documents session_key as 'OpenF1 session identifier.' The description merely repeats this verbatim, adding no additional meaning about format, source, or how to obtain a valid value. Baseline 3 is appropriate.
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 first sentence states a specific verb ('Return'), a specific resource ('driver list'), and a precise scope ('for a specific F1 session'). Combined with the Returns section listing driver_number, full_name, and team, an agent can unambiguously distinguish this from siblings like f1_get_standings or f1_get_race_results.
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 requirement of a session_key implies the agent must first obtain one (likely from f1_get_sessions), but this is never stated. There is no explicit guidance on when to choose this tool over the many F1 siblings that also return driver-related data, such as f1_get_standings or f1_get_race_results.
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.
Sport prefixes make the three domains easy to separate, and most tools have clear purposes. However, several tools overlap: football_match_predictor vs football_xg_model, f1_head_to_head_pace vs f1_race_pace_compare, and the cricket fantasy tools (build_dream11, captain_recommendation, differential_picks) all require careful reading to avoid misselection.
The sport prefix + snake_case pattern is consistent and retrieval tools uniformly use get_, which creates predictability. The main deviation is that many analysis/model tools are noun phrases rather than verb_noun (cricket_head_to_head, football_knockout_path, f1_tyre_degradation), but they remain readable and scoped.
44 tools is well above the 25+ threshold for a single server and will strain agent context and tool-selection quality. Each sport block is individually reasonable at 13-15 tools, but combining three sports plus cross-sport and health utilities makes the overall surface too large.
Core workflows are broadly covered: live data, schedule/standings, match prediction, tournament simulation, fantasy help, and strategy analysis exist for each sport. However, there are notable dead ends: cricket_player_form_index needs a player_id no exposed tool returns, football_get_match_stats requires an API-Football numeric ID not mapped anywhere, and f1_get_lap_times references a stints endpoint that is not exposed as a tool.