Skip to main content
Glama
AlgoChains

AlgoChains MCP Server

Official
by AlgoChains

execute_intent

Destructive

Convert natural language trading requests into actionable plans with constraint solving and execution upon approval.

Instructions

Transform a natural language trading intent into a concrete plan and execute it. Example: 'Get me $10K AI exposure, max 2% per stock'. Parses intent → solves constraints → presents plan for approval → executes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYesNatural language trading intent
dry_runNoIf true, return the plan without executing (default: true for safety)
Behavior4/5

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

Annotations already indicate destructive behavior. The description adds context about the approval step and execution order, but does not detail side effects like trade costs or position changes. It is adequate but not exhaustive.

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

Conciseness5/5

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

The description is two sentences plus an example, with no wasted words. It front-loads the core action and is immediately scannable.

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

Completeness4/5

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

Given the complexity (multi-step approval and execution) and the absence of an output schema, the description provides a reasonable overview. It could clarify return values, but annotations and the example compensate somewhat.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing an example of the 'intent' parameter ('Get me $10K AI exposure...'), which helps the agent understand the expected input format beyond the schema description.

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

Purpose5/5

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

The tool description clearly specifies that it transforms a natural language trading intent into a concrete plan and executes it, using a concrete example. It distinguishes itself from siblings like 'approve_intent' or 'request_trade_confirmation' by encompassing both parsing and execution.

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

Usage Guidelines4/5

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

The description mentions that the tool 'presents plan for approval' before execution, implying a workflow. However, it does not explicitly state when to use this tool versus alternatives like 'approve_intent', leaving some ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AlgoChains/algochains-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server