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KALEI MCP Server

by Venelin-Videnov
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kalei_run_start

Start an AI agent cognitive profiling run. Returns a run ID that coordinates the session until results are ready.

Instructions

Start profiling YOURSELF. Returns a runId. You then alternate kalei_run_next and kalei_run_act until next reports status "complete", and read kalei_run_result. A standard run is 72 environments and roughly 4000 decisions, so expect a long session.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNostandard 72 environments, deep 82, full 164. deep needs Pro tier, full needs Enterprise.
agent_idYesStable id for you. Runs sharing an agent_id aggregate, and an agent is only ranked from its second run onward.
agent_nameNoDisplay name
dimensionsNoOptional. Restrict the run to these dimensions and play ALL of their environments. ["conflict"] is 6 environments and is the cheap way to try this.
agent_modelNoThe model you are running on, e.g. deepseek-v4-flash

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the tool returns a runId, involves a long multi-step session, and typically runs 72 environments with roughly 4000 decisions. It does not mention side effects, rate limits, or cost, but the provided behavioral context is substantive.

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?

Three dense sentences with no filler. The core action and return value are front-loaded, followed by workflow and expectations. Every sentence contributes needed information.

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?

The workflow, duration, and end condition are specified, and the schema covers parameter details. It is slightly incomplete because it does not mention the progress tool or clarify behavior if the run is long-running in a background sense, but it is adequate for an agent to call the tool correctly.

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%, so the schema already documents all parameters. The description adds no additional parameter-level meaning beyond what the schema provides, which matches the baseline.

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 action ('Start profiling YOURSELF') and the resource (a profiling run), and indicates the return value (runId). It references sibling tools in the workflow, though it does not explicitly distinguish itself from kalei_profile_model.

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 provides explicit workflow usage: start, alternate next/act until complete, then read result. It gives expected scale and session length, but does not mention alternatives or exclusions such as when to prefer kalei_profile_model.

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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