How do I install the Agent Reasoning MCP skill?

Run npx skillmds@latest add putervision/agent-reasoning-mcp in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agent Reasoning MCP safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Agent Reasoning MCP?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agent Reasoning MCP free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Agent Reasoning MCP?

putervision (@putervision) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agent Reasoning MCP by putervision — Agent Skill

Name: Agent Reasoning MCP
Rating: 4.2 (5 reviews)
Author: putervision

File contents

Strategic Agent Reasoning (agent-reasoning-mcp)

This skill provides step-by-step guidance and operational patterns for interacting with @putervision/agent-reasoning-mcp with project slug "world-model-mcp".

1. Role in the PuterVision Pentad

Workflow State (state-memory-mcp): Persistent task DAGs, decisions, milestones, and blockers.
Perception (vision-memory-mcp): Visual layout caching, screenshots, and visual specifications.
Spatial World (world-model-mcp): Persistent 3D/2D coordinates, bounding boxes, and topological relations.
Strategic Reasoning (agent-reasoning-mcp): BDI goal decomposition, multi-attribute expected utility calculation, belief decay, risk assessment, and replanning.
Tactical Execution (behavior-mcp): Deterministic ~60Hz browser behavior tree execution and reactive preemption.

2. Core Operational Sequence

Initialize Objectives: Call set_goal with action: "create" to define top-level goals and action: "decompose" to establish subgoals.
Configure Utility Profile: Tune agent priorities using set_utility_weights (aggression, caution, greed, exploration).
Situational Trade-off Scoring: Call evaluate_situation with action: "snapshot" to rank candidate actions using Pareto utility theory.
Intention Dispatch: Translate chosen action into an execution directive via manage_intentions.
Reactive Replanning: If an unexpected obstacle or blocker emerges, invoke replan.

3. Complete 10 Consolidated MCP Tools Reference

Tool Name	Key Actions	Key Parameters	Description
`set_goal`	`create`, `update`, `get`, `list`, `decompose`, `archive`	`title`, `description`, `priority`, `parent_id`, `subgoals`	Hierarchical BDI goal management and task DAG decomposition.
`evaluate_situation`	`snapshot`, `quick`	`snapshot`, `candidates`, `utility_profile`	Multi-attribute utility evaluation ranking candidate actions from environment state.
`replan`	`blocker`, `recovery`, `alternative`	`goal_id`, `blocker_description`, `strategy`	Adaptive DAG reconstruction and alternative path discovery upon obstacles.
`assess_risk`	`assess`, `matrix`	`hazards`, `tolerance`, `mitigations`	Quantitative threat matrix and probabilistic risk scoring.
`query_knowledge`	`search`, `lookup`, `heuristics`	`query`, `category`, `tags`	Knowledge retrieval of past decision heuristics and domain heuristics.
`set_utility_weights`	`configure`, `get`, `list`, `profile`	`name`, `weights` (aggression, caution, greed, exploration)	Utility weight tuning and personality profile management.
`get_decision_trace`	`get`, `list`, `explain`	`trace_id`, `limit`	Explainable chain-of-thought rationale playback and auditing.
`manage_beliefs`	`set`, `get`, `decay`, `list`	`key`, `value`, `confidence`, `decay_rate`	Structured belief state with temporal exponential confidence decay.
`manage_intentions`	`create`, `get`, `list`, `dispatch`, `cancel`	`goal_id`, `behavior_name`, `parameters`	Execution directives queue connecting strategic plans to runtime engines.
`manage_reasoning_db`	`stats`, `audit`, `snapshot`, `restore`, `prune`	`action`, `name`, `description`	Database diagnostics, snapshots, and SHA-256 Merkle audit verification.

---name: agent-reasoning-mcpdescription: Teaches the agent to use the Strategic Agent Reasoning MCP server for BDI goals, utility scoring, risk evaluation, and replanning.---# Strategic Agent Reasoning (agent-reasoning-mcp)This skill provides step-by-step guidance and operational patterns for interacting with `@putervision/agent-reasoning-mcp` with project slug `"world-model-mcp"`.---## 1. Role in the PuterVision Pentad- **Workflow State** (`state-memory-mcp`): Persistent task DAGs, decisions, milestones, and blockers.- **Perception** (`vision-memory-mcp`): Visual layout caching, screenshots, and visual specifications.- **Spatial World** (`world-model-mcp`): Persistent 3D/2D coordinates, bounding boxes, and topological relations.- **Strategic Reasoning** (`agent-reasoning-mcp`): BDI goal decomposition, multi-attribute expected utility calculation, belief decay, risk assessment, and replanning.- **Tactical Execution** (`behavior-mcp`): Deterministic ~60Hz browser behavior tree execution and reactive preemption.---## 2. Core Operational Sequence1. **Initialize Objectives**: Call `set_goal` with `action: "create"` to define top-level goals and `action: "decompose"` to establish subgoals.2. **Configure Utility Profile**: Tune agent priorities using `set_utility_weights` (aggression, caution, greed, exploration).3. **Situational Trade-off Scoring**: Call `evaluate_situation` with `action: "snapshot"` to rank candidate actions using Pareto utility theory.4. **Intention Dispatch**: Translate chosen action into an execution directive via `manage_intentions`.5. **Reactive Replanning**: If an unexpected obstacle or blocker emerges, invoke `replan`.---## 3. Complete 10 Consolidated MCP Tools Reference| Tool Name | Key Actions | Key Parameters | Description ||---|---|---|---|| `set_goal` | `create`, `update`, `get`, `list`, `decompose`, `archive` | `title`, `description`, `priority`, `parent_id`, `subgoals` | Hierarchical BDI goal management and task DAG decomposition. || `evaluate_situation` | `snapshot`, `quick` | `snapshot`, `candidates`, `utility_profile` | Multi-attribute utility evaluation ranking candidate actions from environment state. || `replan` | `blocker`, `recovery`, `alternative` | `goal_id`, `blocker_description`, `strategy` | Adaptive DAG reconstruction and alternative path discovery upon obstacles. || `assess_risk` | `assess`, `matrix` | `hazards`, `tolerance`, `mitigations` | Quantitative threat matrix and probabilistic risk scoring. || `query_knowledge` | `search`, `lookup`, `heuristics` | `query`, `category`, `tags` | Knowledge retrieval of past decision heuristics and domain heuristics. || `set_utility_weights` | `configure`, `get`, `list`, `profile` | `name`, `weights` (aggression, caution, greed, exploration) | Utility weight tuning and personality profile management. || `get_decision_trace` | `get`, `list`, `explain` | `trace_id`, `limit` | Explainable chain-of-thought rationale playback and auditing. || `manage_beliefs` | `set`, `get`, `decay`, `list` | `key`, `value`, `confidence`, `decay_rate` | Structured belief state with temporal exponential confidence decay. || `manage_intentions` | `create`, `get`, `list`, `dispatch`, `cancel` | `goal_id`, `behavior_name`, `parameters` | Execution directives queue connecting strategic plans to runtime engines. || `manage_reasoning_db` | `stats`, `audit`, `snapshot`, `restore`, `prune` | `action`, `name`, `description` | Database diagnostics, snapshots, and SHA-256 Merkle audit verification. |

Frequently asked questions

Agent Reasoning MCP