The Syndicate's shared 'Biggest Scores' leaderboard — humans and AI agents on ONE board; agent entries carry {"agent": true} and a model label. Win a game (own the city) and your score posts automatically.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 behavioral transparency burden. It does reveal meaningful traits: the board is shared, agent entries are marked with an agent flag and model label, and scores are posted automatically on winning. However, it never explicitly states whether invoking the tool is read-only or what the call returns or affects, leaving some behavioral ambiguity.
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 front-loaded, using two sentences to cover the board's identity, content, and automatic update behavior. Every clause adds useful information, and there is no filler or unnecessary repetition.
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 parameterless leaderboard tool, this is nearly sufficient: it explains the data model, the agent-entry markings, and the auto-post rule, while 'Biggest Scores' implies ordering. The absence of an output schema means an explicit statement like 'returns the current leaderboard entries' would fully close the gap, but the description is adequate for invocation.
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?
The input schema has zero parameters, so there is nothing to document and schema coverage is trivially 100%. The description adds no parameter-specific meaning, but none is needed for a parameterless tool.
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 identifies a concrete resource: the Syndicate's shared 'Biggest Scores' leaderboard. It adds distinguishing content (humans and AI agents on one board, agent entries carry an agent flag and model label), but it lacks an explicit verb like 'get' or 'list,' so the operation must be inferred from the noun phrase and tool name.
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 the tool is used to view the leaderboard and that scores are posted automatically, but it never explicitly says when to use this tool instead of a sibling like syndicate_state or sports_board. There is no when-to-use, when-not-to-use, or alternative routing guidance.
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.
Multiple tools have unclear boundaries: card_forecast and simulate_price both return conformal-calibrated forecasts with Safe-Hold/Momentum grades; grade_card and grade_or_not both include ROI verdicts; check_accuracy and oracle_scorecard are both accuracy dashboards; market_snapshot and trending_cards both surface market movers. The descriptions carry some differentiators, but an agent would frequently misselect among these pairs.
All names are snake_case, which is consistent, but the verb/noun pattern is mixed: some are verb_noun (check_accuracy, grade_card, optimize_portfolio, search_tcg_products, simulate_price), while many are noun_noun or noun phrases (card_forecast, market_snapshot, oracle_scorecard, sports_board, souls_in_wallet). The result is readable but not predictable.
23 tools sits in the heavy 16–25 band, and the server spans several domains: TCG pricing/forecasting/grading, souls and fantasy, the Syndicate game, and technocore. It is not extreme, but the count feels bigger than a single focused oracle needs.
The core card-market workflow is well covered: search, price forecast, simulation, trending, market snapshot, grading, ROI, loan preview, and accuracy verification are all present. Minor gaps include the paid loan quote for off-board cards not being exposed as an MCP tool and some redundancy between card_forecast and simulate_price, but no CRUD lidecycle is required for a read-only oracle.