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simulate_raw_transactions

Simulate Algorand blockchain transactions to test outcomes before execution, using base64-encoded data on mainnet, testnet, or localnet networks.

Instructions

Simulate raw transactions

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
txnsYesArray of transactions to simulate
networkNoAlgorand network to use (default: mainnet)
itemsPerPageNoNumber of items per page for paginated responses (default: 10)
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Simulate raw transactions' gives no insight into what the simulation does (e.g., whether it validates, estimates costs, predicts outcomes, or returns detailed logs), what 'raw' implies (e.g., unprocessed, encoded), or any side effects, permissions, or rate limits. This is inadequate for a tool that likely involves complex transaction processing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

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

While concise with only three words, this is under-specification rather than effective brevity. The description fails to provide necessary context or front-load key information, leaving the agent to guess at the tool's purpose and behavior. Every word should earn its place, but here the words are insufficient.

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

Completeness1/5

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

Given the complexity of simulating transactions (likely involving validation, execution preview, or outcome analysis) and the absence of annotations and output schema, the description is severely incomplete. It doesn't explain what simulation entails, what results to expect, or how this differs from other transaction tools. This leaves critical gaps for an AI agent to use the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100%, with clear documentation for all three parameters (txns as base64-encoded transactions array, network as Algorand network with enum, itemsPerPage for pagination). The description adds no parameter semantics beyond the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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

Purpose2/5

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

The description 'Simulate raw transactions' is essentially a tautology that restates the tool name. While it indicates the tool performs simulation on transactions, it doesn't specify what simulation entails (e.g., dry-run execution, validation, outcome prediction) or what 'raw' means in this context. It doesn't distinguish this tool from sibling tools like 'simulate_transactions' or other transaction-related tools.

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

Usage Guidelines1/5

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

The description provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, appropriate contexts, or comparison to sibling tools like 'simulate_transactions' or 'send_raw_transaction'. The agent must infer usage solely from the name and schema.

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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