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agloop-mcp

by zebbern

agloop_get_agent_info

Retrieve the full agent prompt and definition from .agent.md files to understand agent capabilities and configurations within the AgLoop framework.

Instructions

Read the definition file (.agent.md) for a specific agent. Returns the full agent prompt including frontmatter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for agloop_get_agent_info, which reads agent information using the _sm() state manager.
    @mcp.tool()
    def agloop_get_agent_info(agent_name: str) -> str:
        """Read the definition file (.agent.md) for a specific agent. Returns the full agent prompt including frontmatter."""
        info = _sm().get_agent_info(agent_name)
        if not info:
            return json.dumps({"error": f"Agent '{agent_name}' not found"})
        return info
Behavior2/5

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. While it states the tool reads a file and returns content, it lacks details on error handling (e.g., what happens if the agent doesn't exist), performance characteristics, or any side effects. The description is minimal and doesn't compensate for the absence of annotations.

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 a single, well-structured sentence that efficiently conveys the core functionality without unnecessary words. It is front-loaded with the main action and result, making it easy to understand quickly.

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

Completeness3/5

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

Given the tool's simplicity (one parameter, read-only operation) and the presence of an output schema (which likely describes the return structure), the description is somewhat complete but lacks depth. It doesn't cover error cases or usage context, which would be helpful for an agent to invoke it correctly without annotations.

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?

With 0% schema description coverage and only one parameter, the description doesn't add specific details about the 'agent_name' parameter (e.g., format, examples, or where to find valid names). However, the context implies it refers to an agent whose definition file exists, providing some semantic context. Since there's only one parameter, the baseline is high, but the description could be more informative.

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 specific action ('Read the definition file'), identifies the resource ('.agent.md file for a specific agent'), and distinguishes it from siblings like 'agloop_list_agents' (which lists agents rather than reading their definition files). It provides a complete picture of what the tool does.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether the agent must exist), contrast it with 'agloop_list_agents' (which might be used to discover available agents first), or specify scenarios where this tool is appropriate versus not.

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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