Returns the full documentation and workflow guide for this MCP server. Call this first to understand all available tools, their use cases, and how to combine them. Includes: tool catalog, recommended workflows for Polymarket research, historical analog analysis, and example calls.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Focus area: 'all' for full guide, or a specific topic | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It clearly states that the tool returns documentation and includes a catalog, workflows, and examples, implying read-only behavior. It does not claim any side effects or mutations, and its purpose is inherently non-destructive.
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 concise, using three sentences that front-load the main purpose ('Returns the full documentation...'), followed by usage guidance and content highlights. Every sentence adds value with no extraneous information.
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 that the tool is a meta-documentation guide with a single optional parameter and no output schema, the description covers the essential context: what it returns, what it includes, and when to call it. It could mention the output format (e.g., plain text vs. structured), but that is a minor omission.
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 100% description coverage for the 'topic' parameter, with enum values and a clear description. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline of 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 description clearly states the tool's function: 'Returns the full documentation and workflow guide for this MCP server.' This uses a specific verb ('Returns') and resource, and distinguishes it from sibling tools that perform specific tasks like backtesting or pattern search.
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 explicitly instructs 'Call this first to understand all available tools, their use cases, and how to combine them.' This provides clear when-to-use guidance and establishes it as the entry point, eliminating confusion with other tools.
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 tools inhabit overlapping territory: find_market_analogs, pattern_search, search_by_sketch, and get_candle_market_snapshot all relate to historical pattern matching, while get_trading_decision, get_trader_decision_v2, and get_live_polymarket_trade_decision all produce trade-oriented decisions. The descriptions add context, but an agent could still easily pick the wrong tool for a given request.
The tools are consistently snake_case and mostly readable, but the naming conventions are mixed: many tools use get_<noun>, while others start with verbs like backtest, detect, find, forecast. Minor irregularities such as pattern_search and the v2 suffix in get_trader_decision_v2 also reduce predictability.
Fifteen tools is within a reasonable size, and the server covers a broad domain: pattern search, regime detection, backtesting, track records, private datasets, live Polymarket decisions, and documentation. The count is not excessive, but some tools are functionally redundant enough that the set could be tightened.
The tool surface covers the main evidence workflow well: discovering patterns, analyzing analogs, backtesting strategies, checking track records, and producing trading decisions. Minor gaps remain around private dataset management and there is no separate low-level raw candle query tool, but most core user journeys are supported.
