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finance_investment_paper_trade_runbook

Destructive

Execute the investment paper trade runbook to simulate trades and validate strategies using free-text objectives and structured inputs.

Instructions

Run the finance domain agent action investment_paper_trade_runbook.

Routes through the platform's domain-agent dispatcher under your JWT, tenant, and company scope.

Args: message: Free-text objective for the action. inputs: Optional JSON string of structured inputs for the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputsNo{}
messageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. Addedv0.1.1

TDQS

C2.7/5.0
Behavior3/5

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

It adds some behavioral context beyond the annotations by disclosing that the call routes through the domain-agent dispatcher under JWT, tenant, and company scope. However, it does not explain what executing the runbook does, what side effects may occur, or how the `destructiveHint: true` annotation should be interpreted. There is no contradiction with 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.

Conciseness4/5

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

The description is compact and front-loaded, with a scannable Args block and no filler. It could carry more substance, but as written it is efficient and easy to parse.

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

Completeness2/5

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

For a generic dispatcher tool with two free-form parameters and destructive annotations, this description is too thin. An agent cannot determine what objective to place in `message`, what `inputs` should contain, or what outcomes or risks to expect. The output schema reduces the need to explain return values, but invocation semantics remain underspecified.

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

Parameters2/5

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

With 0% schema description coverage, the description needs to carry the parameter-semantics burden. It provides minimal roles: `message` is a free-text objective and `inputs` is an optional JSON string. That is helpful but still thin, because it does not specify what structured inputs are expected, what keys or format to use, or how `message` and `inputs` interact.

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

Purpose3/5

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

The description states the verb and resource clearly: it runs the `investment_paper_trade_runbook` finance domain-agent action and routes it through the platform dispatcher. However, it never explains what that action actually accomplishes, so the purpose is only as clear as the opaque action name itself. It also does not differentiate it from the many sibling finance runbook and trading tools.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives such as `finance_investment_trading_agent`, `finance_investment_paper_observation_program`, or other finance runbooks. The description only says it runs a specific action, which implies use but provides no scenario, prerequisites, or exclusions.

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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