Skip to main content
Glama

prompt_agent

Send messages to Letta agents and receive responses by specifying agent IDs and messages, enabling interaction with AI agents through the Letta MCP Server.

Instructions

Send a message to an agent and get a response. Ensure the agent has necessary tools attached (see attach_tool) first. Use list_agents to find agent IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesID of the agent to prompt
messageYesMessage to send to the agent

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
usageNo
messagesYes
Behavior4/5

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

Annotations only provide a title ('Send Message to Agent'), so the description carries the full burden. It adds valuable behavioral context: the tool requires tools to be attached first (a prerequisite), mentions using 'list_agents' for IDs (a setup step), and implies it's for interactive messaging (not destructive). However, it doesn't specify rate limits, auth needs, or response format details, leaving some gaps.

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 front-loaded with the core purpose in the first sentence, followed by two concise prerequisite and setup sentences. Each sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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 tool's complexity (interactive messaging with prerequisites), 100% schema coverage, and an output schema (implied by 'get a response'), the description is mostly complete. It covers purpose, usage, and prerequisites adequately. However, it lacks details on behavioral aspects like error handling or response structure, which could be useful despite the output schema.

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 descriptions for 'agent_id' and 'message'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding.

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 description clearly states the action ('Send a message to an agent and get a response') and the resource ('an agent'), with specific verbs and object. It distinguishes from siblings like 'list_agents' (which lists agents) and 'attach_tool' (which attaches tools), making the purpose explicit and differentiated.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool ('Send a message to an agent and get a response'), prerequisites ('Ensure the agent has necessary tools attached (see attach_tool) first'), and alternatives ('Use list_agents to find agent IDs'), covering when, when-not, and related tools comprehensively.

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/oculairmedia/Letta-MCP-server'

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