Return weather data for a specific F1 session.
Args: session_key: OpenF1 session identifier.
Returns: data.weather: list of weather snapshots with temperature, rainfall, wind. 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 establish this as a safe, read-only, idempotent operation. The description adds meaningful behavioral detail by specifying the return shape: a list of weather snapshots with temperature, rainfall, and wind, plus a meta.source field. This goes beyond the annotation-only baseline without contradicting any hints.
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 reasonably concise, front-loads the core purpose in the first sentence, and uses an Args/Returns structure that is easy to parse. It slightly redundantly repeats the parameter description from the schema, but overall it is efficient and well organized.
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, read-only data retrieval tool with a known output schema, the description covers the essential inputs and return fields. It is complete enough for an agent to call correctly, though it could optionally note that session_key comes from f1_get_sessions. The presence of an output schema reduces the need to document return values in detail.
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%, so the schema already documents session_key as an OpenF1 session identifier. The description repeats the exact same phrase, adding no new semantic meaning. The baseline of 3 is appropriate since the schema fully handles parameter documentation.
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 opens with a specific verb and resource: 'Return weather data for a specific F1 session.' It clearly distinguishes from sibling tools like f1_weather_strategy_impact (which analyzes strategy impact) and f1_get_sessions (session metadata), making the tool's function unambiguous.
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 what the tool does but provides no guidance on when to use it versus alternatives. It does not mention that a session_key must first be obtained via f1_get_sessions, nor does it contrast with f1_weather_strategy_impact, which would help an agent choose the right weather-related tool.
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.