get_eventsUpcoming + live events for a league (without odds — useful to discover event IDs).
| Name | Required | Description | Default |
|---|---|---|---|
| event_ids | No | ||
| sport_key | Yes | ||
| date_format | No |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "sport_key": "americanfootball_nfl"
+ },
+ {
+ "date_format": "iso",
+ "sport_key": "basketball_nba"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "items": {
+ "properties": {
+ "away_team": {
+ "description": "Away team name",
+ "type": "string"
+ },
+ "commence_time": {
+ "description": "Event start time (ISO 8601 or unix)",
+ "type": "string"
+ },
+ "home_team": {
+ "description": "Home team name",
+ "type": "string"
+ },
+ "id": {
+ "description": "Event ID",
+ "type": "string"
+ },
+ "sport_key": {
+ "description": "Sport key",
+ "type": "string"
+ },
+ "sport_title": {
+ "description": "Sport title",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, read-only, idempotent behavior. Description adds context: returns upcoming+live events, no odds, and purpose for ID discovery. No contradictions.
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?
Single sentence, front-loaded with key info, no filler. Every word earns its place.
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?
Given existence of output schema, description need not explain return values. However, with 3 undocumented parameters and no schema description, some gaps remain. Adequate for a simple tool but not fully complete.
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 0%, so description must compensate but provides no parameter details. Purpose is clear but does not explain sport_key, event_ids, or date_format formats. Output schema exists but not referenced.
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?
Description clearly specifies verb (get), resource (events for a league), and distinguishes from siblings like get_event_odds by stating 'without odds'. It also explains the utility (discover event IDs).
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?
Description implicitly advises when to use (when event IDs needed without odds) by contrasting with odds-including tool. However, it does not explicitly state when not to use or name alternatives like get_event_odds.
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.
Several tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,724 tools with only subtle behavioral differences, and bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_kalshi_spread heavily overlap around prediction-market opportunity discovery. entity_profile, compare_entities, and recent_changes also fan out across the same SEC/news/patent sources, making selection ambiguous for agents.
Naming mixes multiple conventions: snake_case verb_noun for odds tools (get_events, list_sports), vendor-prefixed clusters (ask_pipeworx_*, pipeworx_*, polymarket_*), and a few reversed noun-verb names like bet_research. CamelCase is used in ai_visibility_check and generate_llms_txt adds another style. Only the polymarket_* and pipeworx_* families are internally consistent, but the overall pattern is chaotic.
37 tools is well beyond the typical well-scoped server, and the count feels inflated by unrelated meta-tools (suggest_questions, discover_tools, pipeworx_feedback, pipeworx_trending, generate_llms_txt, scan_dependency, remember/recall/forget) that have nothing to do with the server's stated 'Odds Api' purpose. The actual odds surface is only ~5 tools, so the vast majority of the catalog is off-scope padding.
The core odds domain is well covered: list_sports, get_events, get_odds, get_event_odds, and get_scores form a coherent lifecycle, plus quota introspection. However, for the server's actual broad-research scope there are noticeable gaps (e.g., no direct single-filing fetch tool despite heavy SEC coverage, a lone npm-dependency tool with no surrounding ecosystem, and no historical/past-odds endpoint), and the heterogeneous domains make completeness uneven.