DepthFeed

limitless_get_snapshots

Read-only

Historical L2 depth snapshots (time series) for a Limitless market by slug. Every account plan; history is bounded by your plan window.

Input Schema

TableJSON Schema

Name	Required	Description
`fill`	No	ffill returns ONE ROW PER BUCKET for the whole window, carrying the last captured book into buckets that held no capture. Each row then carries `filled` (0 = a capture, 1 = carried forward) and `as_of_ts`, the moment that book was recorded. Requires `interval`. Without it, `interval` returns only the buckets that held a capture — a short series means the market is quoted infrequently, not that data is missing.
`slug`	Yes	Limitless market slug.
`limit`	No	Page size (1–1000).
`cursor`	No	Opaque pagination cursor from a previous response's pagination.next_cursor.
`end_time`	No	End of time as ms-epoch or ISO-8601. History is clamped to your plan's window.
`interval`	No	Downsample bucket — 30s, 1m, 5m, 1h, up to 1d. Returns the newest book in each bucket. Omit for every recorded book change.
`start_time`	No	Start of time as ms-epoch or ISO-8601. History is clamped to your plan's window.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed2 schema fields changedSep 2, 2026

addedInput schema / properties / fill

Added value: +{
+  "description": "ffill returns ONE ROW PER BUCKET for the whole window, carrying the last captured book into buckets that held no capture. Each row then carries `filled` (0 = a capture, 1 = carried forward) and `as_of_ts`, the moment that book was recorded. Requires `interval`. Without it, `interval` returns only the buckets that held a capture — a short series means the market is quoted infrequently, not that data is missing.",
+  "enum": [
+    "ffill",
+    "none"
+  ],
+  "type": "string"
+}

addedInput schema / properties / interval

Added value: +{
+  "description": "Downsample bucket — 30s, 1m, 5m, 1h, up to 1d. Returns the newest book in each bucket. Omit for every recorded book change.",
+  "type": "string"
+}

First observedJun 29, 2026

TDQS

A3.8/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark the tool as readOnlyHint=true, so the safety profile is covered. The description adds that history is bounded by the plan window, which is useful behavioral context. It does not disclose return shape, pagination behavior, or potential rate limits, but the schema fields (e.g., cursor) partly cover pagination. The extra detail is minor but non-contradictory.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short sentences with no filler. The first sentence establishes the tool's core function and resource scope, while the second conveys the plan-window constraint. Every word earns its place, and the key differentiator ('Historical') is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the readOnlyHint and openWorldHint annotations, plus a fully documented input schema covering all 7 parameters, the description is mostly complete for a data-retrieval tool. The only minor gap is the lack of a return-value description, but no output schema exists and the tool's nature (snapshots) implies the general shape. The essential call-time information is present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with every parameter individually documented in the input schema. The description itself adds no parameter-specific information, so it relies on the schema to carry semantic weight. Per the baseline for high schema coverage, a score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as retrieving historical L2 depth snapshots (time series) for a Limitless market by slug. The term 'Historical' differentiates it from real-time order book tools like limitless_get_orderbook, and 'by slug' specifies the resource identifier. The scope is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description notes that 'Every account plan' can use it and that history is bounded by the plan window, providing some context on applicability. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or sibling tools for comparison. The usage guidance is implied rather than directed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0

Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness5/5

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.

Resources

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