apex_run_pyfolio_tearsheetsRun the allowlisted permissionless bounded pyfolio-reloaded 0.9.9 tear-sheet core over supplied dated returns. DATA ONLY, read-only, no HMAC required, no plots, no network, no orders, no wallet/account access.
| Name | Required | Description | Default |
|---|---|---|---|
| returns | Yes | ||
| periods_per_year | 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 far beyond the readOnlyHint annotation by explicitly enumerating behavioral constraints: 'DATA ONLY, read-only, no HMAC required, no plots, no network, no orders, no wallet/account access.' It also notes the execution is 'allowlisted permissionless bounded', which reassures about side effects and resource limits. This adds substantial value over the annotations.
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 a single dense sentence that front-loads the core purpose and then packs safety constraints. While every phrase carries information, the string of hyphenated qualifiers ('allowlisted permissionless bounded') and repeated negatives ('no network, no orders, no wallet/account access') makes it slightly less streamlined than optimal.
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 description thoroughly covers safety and execution context, which is especially valuable given the annotation set. However, with no output schema, it omits what the tool returns (e.g., tear-sheet metrics, summary object) and any assumptions about input data (e.g., sorted dates), leaving some ambiguity for the agent about expected results.
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%, so the description must compensate for parameter meaning, but it only says 'dated returns'—which mostly repeats the parameter name. It does not clarify the expected scale (decimal vs percentage), the need for chronological ordering, or the meaning of the fixed 'periods_per_year' const, leaving the agent to rely solely on schema constraints.
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 names a specific verb ('Run') and a specific resource ('pyfolio-reloaded 0.9.9 tear-sheet core') over 'dated returns', which clearly distinguishes it from sibling tools like apex_run_empyrical_metrics or apex_run_alphalens_factor_research. The qualifiers 'data only' and 'no plots' further disambiguate its scope.
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 implies when to use the tool—when you need a pyfolio tear-sheet over returns—and its constraints (permissionless, bounded, no network) suggest safe usage. However, it does not explicitly state when not to use it or name alternatives, leaving the agent to infer usage from the tool name and limited context.
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.