agent-reasoning-mcp
Skill24 starsChanged 49 days agoMITView on GitHub
Teaches the agent to use the Strategic Agent Reasoning MCP server for BDI goals, utility scoring, risk evaluation, and replanning.
4 other projects use a version of this, 4 of them edited.
Adopted by
Most recently taken up by
- putervision/vision-memory-mcpedited, first seen Oct 8, 2026
- putervision/state-memory-mcpedited, first seen Sep 28, 2026
- putervision/world-model-mcpedited, first seen Sep 26, 2026
- putervision/agent-reasoning-mcpedited, first seen Sep 26, 2026
Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 4
- putervision/state-memory-mcp★ 11497% same
- putervision/world-model-mcp★ 5099% same
- putervision/vision-memory-mcp★ 4897% same
- putervision/agent-reasoning-mcp★ 2299% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
- .agents/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- .cursor/rules/agent-reasoning-mcp.mdcCursor rule
- .cursor/rules/behavior-mcp.mdcCursor rule
- .cursor/rules/state-memory-mcp.mdcCursor rule
- .cursor/rules/vision-memory-mcp.mdcCursor rule
- .cursor/rules/world-model-mcp.mdcCursor rule
- docs/llms.txtllms.txt
- behavior-mcpSkill
- state-memory-mcpSkill
- video-ingestSkill
- vision-memory-mcpSkill
- webcrypt-mcpSkill
- world-model-mcpSkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 0 are distinctive, and 3 are very specific to this project.
0
Common
Most of the 4 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 22
Initialize Objectives: Call set_goal with action: "create" to define top-level goals and action: "decompose" to establish subgoals.
- Very specific: line 24
Situational Trade-off Scoring: Call evaluate_situation with action: "snapshot" to rank candidate actions using Pareto utility theory.
- Very specific: line 26
Reactive Replanning: If an unexpected obstacle or blocker emerges, invoke replan.
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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 "behavior-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_goalwithaction: "create"to define top-level goals andaction: "decompose"to establish subgoals. - Configure Utility Profile: Tune agent priorities using
set_utility_weights(aggression, caution, greed, exploration). - Situational Trade-off Scoring: Call
evaluate_situationwithaction: "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 15 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. |
classify | evaluation | category, input, taxonomy, state_pack | Zero-LLM deterministic classification against hierarchical taxonomy (<2ms SLA). |
ask_noul | evaluation | condition, state_pack, threshold | Fast binary (Yes/No/Abstain) heuristic gate evaluating conditions (<2ms SLA). |
ask_choice | evaluation | choices, context, state_pack | Deterministic multi-alternative selection ranking candidate choices (<2ms SLA). |
ask_score | evaluation | target, metric, scale, state_pack | Heuristic utility evaluation scoring target entities on a bounded scale (<2ms SLA). |
gate_intention | evaluation | project, proposed_action, state_pack | Fast-path safety & feasibility filter checking preconditions before execution (<1ms SLA). |
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.