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Swarm

by io.github.blackscaletech
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Get learning frontier

swarm_get_learning_frontier
Read-onlyIdempotent

Review stored learning queues, candidate/projection statuses, promotion decisions and validation scores. Use swarm_list_learning_queue or swarm_list_learning_candidates for individual items. No learning or promotion starts.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "swarm_get_learning_frontier"
  }
}

Input Schema

NameRequiredDescriptionDefault
space_idNoSpace ID from swarm_list_spaces; omit if sole.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / space_id / description
      Previous value: -"Omit for a single approved Space."New value: +"Space ID from swarm_list_spaces; omit if sole."
  2. Changed1 schema field changed
    • addedInput schema / properties / space_id / description
      Added value: +"Omit for a single approved Space."
  3. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds value beyond them by asserting that no learning or promotion is triggered, which is the main side effect an agent might fear from a 'frontier'/promotion-related tool. Return shape is left to the output schema, which is fair.

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?

Two short sentences, front-loaded with the scope of what is returned, followed by the disambiguation pointer. Every sentence carries distinct information with no filler.

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?

With rich annotations, an output schema, and one optional documented parameter, the description covers what an agent needs: the aggregate contents and the absence of side effects. It could have stated the point at which an agent should refresh or snapshot the frontier, but nothing essential is missing.

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?

There is a single optional space_id parameter and schema coverage is 100%, including the 'omit if sole' guidance. The description adds nothing about the parameter, which is acceptable at baseline 3 when the schema fully documents it.

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 names a specific resource (learning frontier) and enumerates the exact contents an agent will see: queues, candidate/projection statuses, promotion decisions, validation scores. It is distinguishable from the list siblings by naming them. 'Review' is a slightly soft verb for an aggregate/dashboard view, which keeps it just below a 5.

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?

It explicitly routes item-level needs to swarm_list_learning_queue and swarm_list_learning_candidates, which is a concrete alternative selection rule. 'No learning or promotion starts' also sets expectations about what this tool is not for. It stops short of stating plainly when to prefer this aggregate view, so a 4 rather than 5.

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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