Latest L2 depth (bids desc / asks asc) for a Limitless market by slug. Every account plan; only the keyless demo surface is refused.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Limitless market slug from limitless_search_markets. |
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?
Beyond the readOnlyHint and openWorldHint annotations, the description adds meaningful behavioral details: L2 depth, ascending/descending order of levels, and which access surface is restricted. This helps the agent know what to expect without over-explaining.
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?
One compact sentence packs the resource, format, ordering, and access constraints with zero redundancy. Every clause earns its place and the key behavior is front-loaded.
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 the tool's simplicity (one param, no output schema), the description covers the essential return shape (bids desc / asks asc) and access rules. It lacks explicit mention of pagination or response envelope, but for a depth snapshot this is adequate.
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% for the single slug parameter, already specifying its source (limitless_search_markets). The description adds no extra semantics, which is acceptable given the schema carries the full meaning.
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 gets the latest L2 order book depth for a Limitless market by slug, with explicit ordering of bids and asks. It distinguishes itself from sibling tools like kalshi_get_orderbook by naming the Limitless domain.
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?
It provides context on accessibility ('Every account plan; only the keyless demo surface is refused') and implies the slug must come from limitless_search_markets, giving a clear workflow hint. It does not explicitly contrast with limitless_get_snapshots or other alternatives, but the usage context is clear enough.
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.
Each tool targets a distinct venue and data type: Polymarket/Kalshi/Limitless-specific search, orderbook, and snapshot tools are clearly separated by prefix, while bars, backtest_paths, screener, and whoami serve unique purposes. No two tools could plausibly be confused in agent selection.
Most tools follow a clear `<venue>_<action>_<object>` pattern (e.g., kalshi_get_orderbook, limitless_get_snapshots, polymarket_search_markets). A few like backtest_paths, bars, screener, and whoami break the pattern, but they are descriptive and consistent with their domain. Overall predictable and legible.
15 tools is well-scoped for a multi-venue market data API. Each tool adds meaningful functionality—search, snapshots, candles, orderbooks, screener, session info—without redundancy. The count aligns well with the apparent coverage of prediction markets, crypto, and equities.
The surface covers read operations for all major venues: search, market detail, orderbook, historical snapshots, and OHLCV bars. It also includes backtesting, cross-venue screener, and session introspection. No obvious dead ends or missing critical operations for a data-centric server.
