apex_query_datasetQuery the free bounded stillme dataset sample layer. Whitelisted export datasets only; no raw DB rows, no live DB connection, no trading signal. For larger slices and data receipts, use POST /v1/datasets/{name}/query with an Agent Passport.
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | ||
| sort | No | ||
| limit | No | ||
| start | No | ||
| symbol | No | ||
| dataset | Yes | ||
| decision | No | ||
| strategy | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / dataset / enumPrevious value: -[
- "funding_by_symbol",
- "research_outcomes_agg"
-]New value: +[
+ "funding_by_symbol",
+ "research_outcomes_agg",
+ "ohlcv_daily"
+]Input schema / properties / endAdded value: +{
+ "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
+ "type": "string"
+}Input schema / properties / startAdded value: +{
+ "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already declares safe read; the description adds that it only accesses whitelisted sample data, no raw DB rows, no live connection, and no trading signal, which provides useful behavioral context beyond the annotations. No contradictions.
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?
Two sentences, each informative: the first defines the tool, the second gives an alternative. No filler.
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 query tool with 8 parameters and no output schema, the description covers the core constraints and alternative path, but lacks information about query parameters, return format, and pagination, leaving moderate gaps.
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 0%, and the description only hints at the dataset parameter ('whitelisted export datasets') and 'bounded' which loosely relates to limit. It does not explain start, end, symbol, sort, decision, strategy, or limit semantics, so it fails to compensate for the lack of schema descriptions.
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 queries a bounded dataset sample layer using a specific verb and resource. It distinguishes itself from the larger query API but does not explicitly reference sibling tools like apex_query_cards, so no sibling differentiation.
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 identifies when to use this tool ('whitelisted export datasets only') and directs users to an alternative ('POST /v1/datasets/{name}/query with an Agent Passport') for larger slices and data receipts. This provides clear when-to-use and when-not-to-use 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.
Most tools have clearly distinct purposes, and descriptions provide sufficient boundaries. Some run_* analytics tools (e.g., deflated_sharpe vs empyrical_metrics) could be conceptually confused, but their specific inputs and outputs minimize ambiguity.
All tools share the apex_ prefix, and the verb_noun pattern is consistent (get, query, run, submit). The 'agent_' subgroup within run tools introduces a minor irregularity, but it remains readily comprehensible.
With 24 tools, the server is on the heavy side, falling into the 16-25 range. Many run_* tools are similar in nature (pure calculations), but each appears to serve a specific purpose, so the count is borderline rather than excessive.
The server name implies a card store, yet the tool surface only supports reading and querying cards, with no create, update, or delete operations. This is a significant gap that prevents full lifecycle management, though the analytics side is fairly comprehensive.