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DanielTomaro13

sportsdata-mcp

by DanielTomaro13
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Python
Remote

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    TDQS

    B3.2/5.0

    Scored across 829 tools

    Disambiguation2/5

    Tools are organized by provider prefix and many carry detailed 'also answers this' cross-references, but the same question is served by multiple near-duplicate tools across providers (six SGM pricers, four racing-form guides, four Pinnacle matchup variants, several overlapping MLB/NBA/NHL sources). With 829 tools, an agent will routinely face near-identical choices differentiated only by provider quirks.

    Naming Consistency3/5

    Each provider namespace is internally consistent (afl_* uses _list/_get suffixes, mlb_* uses bare nouns, betr_* uses noun phrases), which keeps things readable. However, conventions are mixed across the server — verb_noun tools like list_available_groups sit beside noun_verb tools like afl_players_list — and there are irregularities like twitter_user_by_username vs twitter_users_by_usernames and pl_players_by_id vs mlb_people.

    Tool Count1/5

    829 tools is an extreme mismatch for a single MCP surface — an order of magnitude beyond even a heavy server. The provider-group gating described by list_available_groups partially mitigates this, but as exposed, the tool set is far too large for any agent to hold in context or navigate reliably.

    Completeness4/5

    For its stated purpose as a sports-data aggregator, coverage is near-total: official and third-party sources across dozens of sports, odds, fantasy platforms, prediction markets, CMS content, and multiple SGM price/validate tools. Minor gaps exist — bet-placement tools are referenced (sportsbet_place_bet, tab_place_bet) but absent from the exposed surface, and some third-party providers are explicitly superseded by deeper official feeds.

    Maintenance

    ActivityActive
    ResponsivenessNo issues

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