Skip to main content
Glama
zebbern

agloop-mcp

by zebbern

agloop_get_next_task

Determine the next task to execute using topological sorting, returning both the recommended task and reasoning for prioritization.

Instructions

Compute the next task using topological sort. Returns the recommended next task and reasoning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The implementation of the `agloop_get_next_task` tool, which utilizes the `StateManager` to compute and return the next task.
    def agloop_get_next_task() -> str:
        """Compute the next task using topological sort. Returns the recommended next task and reasoning."""
        result = _sm().get_next_task()
        data = {
            "task": asdict(result.task) if result.task else None,
            "reason": result.reason,
        }
        return json.dumps(data, indent=2)
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. It mentions the computation method ('topological sort') and return values ('next task and reasoning'), but lacks critical behavioral details: whether this is read-only or mutates state, if it requires specific permissions, how it handles errors, or if it's idempotent. For a tool with zero annotation coverage, this is insufficient.

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 extremely concise and front-loaded: two sentences that directly state the action and output. Every word earns its place, with no redundancy or fluff. It efficiently communicates the core functionality.

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 has 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally adequate. It explains what the tool does and what it returns, but lacks context about when to use it, behavioral traits, or integration with siblings. For a tool with no annotations, it should provide more operational guidance.

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?

The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so a baseline of 4 is appropriate. It correctly indicates no inputs are required.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Compute the next task using topological sort. Returns the recommended next task and reasoning.' It specifies the verb ('compute'), resource ('next task'), and method ('topological sort'), distinguishing it from siblings like agloop_get_task or agloop_list_tasks. However, it doesn't explicitly differentiate from all siblings (e.g., agloop_get_plan might also involve task sequencing).

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, context (e.g., after completing a task, during planning), or exclusions. Siblings like agloop_get_task or agloop_get_plan might overlap in functionality, but no comparison is made.

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/zebbern/agloop-mcp'

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