apex_run_alphalens_factor_researchRun the allowlisted permissionless bounded alphalens-style factor research wrapper over supplied factor rows. Pure TS reimplementation of selected OSS alphalens-reloaded 0.4.5 routines. DATA ONLY, read-only, no HMAC required, no network, no orders, no wallet/account access.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | ||
| options | No | ||
| records | Yes | ||
| function | Yes | ||
| quantile | No |
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?
The description goes beyond the readOnlyHint annotation by explicitly stating 'DATA ONLY, read-only, no HMAC required, no network, no orders, no wallet/account access.' It also discloses that it is a pure TypeScript reimplementation of selected alphalens-reloaded routines, providing significant behavioral context.
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, front-loaded with the core purpose and followed by safety details. It is efficient, though the phrase 'allowlisted permissionless bounded' is slightly redundant with the later 'DATA ONLY, read-only' clarification.
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?
The tool has five parameters including nested records and an enum, and no output schema. The description does not explain parameter semantics, return values, or how the function enum selects behavior. It focuses on safety but omits functional details needed for correct invocation and result interpretation.
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?
With 0% schema description coverage, the description carries the burden of explaining parameters. It mentions 'factor rows' but does not explain the expected structure of records, the meaning of period/options/quantile, or the behavior of the function enum. The enum values are self-explanatory to domain experts but not explicitly documented.
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 runs an 'alphalens-style factor research wrapper' over supplied factor rows, which is a specific verb+resource. It distinguishes itself from siblings by the unique 'alphalens-style' qualifier, though it does not explicitly name alternatives.
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 provides context that it is permissionless, bounded, and data-only, implying it is safe for read-only analysis without auth/network. However, it does not explicitly state when to use it over sibling research tools like empyrical_metrics or pyfolio_tearsheets, nor any exclusions.
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.