list_pending_actionsFinancial actions proposed by your research runs that are waiting for a human decision.
Nothing here has been executed or ever will be by this system. These are proposals.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| hint | No | ||
| count | No | ||
| reason | No | ||
| actions | No | ||
| blocked | No | ||
| not_investment_advice | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable beyond that by explicitly stateing 'Nothing here has been executed or ever will be by this system. These are proposals.' This prevents a common misinterpretation that listing reveals executed or executable actions. No contrdiction with 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 concise: two short sentences, purpose first, with the crucial non-execution clarifiation second. Every sentence adds value and there is no fluf or repetition of schema/annotation data.
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 no-parameter list tool with robust annotations and an output schema, the description is complete. It tells an agent what the list contains, that the items are proposals only, and that the system will never execute them. Nothing important is missing for correct tool selection.
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?
There are zero parameters, so the description has no parameter burden. The schema coverage is vacuously 100%, and the baseline of 4 for a zero-parameter tool applies. The description adds the relevant context about what the returned items represent.
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?
Description states a clear verb ('List') and specific resource ('financial actions proposed by your research runs that are waiting for a human decision'). This differentiates it from siblings like approve_action by emphasizing these are proposals awaiting human input. It precisely defines what the tool returns.
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 clear context for when to use: when an agent needs to see financial actions proposed by research runs that have not yet been decided on. It does not explicitly name alternatives or exclusions, but the 'waiting for a human decision' phrasing makes the intended use context unmistakable.
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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The tools are largely distinct: querying, searching, backtesting, risk reads, alerts, memory, and audit functions each have clear homes. A few adjacent pairs (risk_read vs risk_assess, company_health_check vs positioning_read) could be confused, but the descriptions draw explicit boundaries.
There are strong consistent clusters like list_*, get_*, run_*, and memory_*, but the *_read suffix alternates with noun-first names like company_health_check, and bare-verb tools like ask, calendar, chart, and screen break the pattern. The naming is readable but not uniform.
Forty tools is well past the 25+ threshold and makes the surface heavy for an agent to navigate, even though the breadth reflects a genuinely wide platform. Several clusters could plausibly be consolidated without losing capability.
The tool surface covers the main lifecycle well: discovery, point-in-time querying, filings search and full text, backtesting, research, risk assessment, alerts, memory, approvals, and provenance verification. Minor gaps exist—no strategy management tools, no memory deletion, no bulk export—but agents can work around them.