Agentic RL: Credit Assignment and CLI Agents
Filter agent RL methods by supervision, critic and task setting; retrieve source links and BibTeX.
Should I use this
Quality & Safety
Findings (8)
- LOWTool 'Agentic_RL_list_sources' doesn't follow camelCase/snake_casein Agentic_RL_list_sources
- LOWTool 'Agentic_RL_search_evidence' doesn't follow camelCase/snake_casein Agentic_RL_search_evidence
- LOWTool 'Agentic_RL_fetch_evidence' doesn't follow camelCase/snake_casein Agentic_RL_fetch_evidence
- LOWTool 'Agentic_RL_dataset_overview' doesn't follow camelCase/snake_casein Agentic_RL_dataset_overview
- LOWTool 'Agentic_RL_search_tasks' doesn't follow camelCase/snake_casein Agentic_RL_search_tasks
- LOWTool 'Agentic_RL_get_task' doesn't follow camelCase/snake_casein Agentic_RL_get_task
- LOWTool 'Agentic_RL_list_method_facets' doesn't follow camelCase/snake_casein Agentic_RL_list_method_facets
- LOWTool 'Agentic_RL_filter_methods' doesn't follow camelCase/snake_casein Agentic_RL_filter_methods
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"agentic-rl": {
"url": "https://hoyant-su-agentic-rl.hf.space/gradio_api/mcp/"
}
}
}Remote endpoints
https://hoyant-su-agentic-rl.hf.space/gradio_api/mcp/streamable-httpWhat it can do
Tool inventory
Tools (8)
🟢Agentic_RL_list_sources
List original papers and retrieval coverage. Discover source-linked comparisons of credit assignment, agent memory, selective observation and terminal benchmarks, with JSON, CSV and BibTeX links.
Input Schema
{
"type": "object",
"properties": {}
}🟢Agentic_RL_search_evidence(query, limit)
Search original papers on agentic reinforcement learning, credit assignment and CLI agents. Use English keywords (AND), OR and quoted phrases. Return relevant passages, source citations, equations and table cells.
Input Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": ""
},
"limit": {
"type": "integer",
"description": "",
"default": 5
}
},
"required": [
"query"
]
}🟢Agentic_RL_fetch_evidence(evidence_id)
Fetch a complete original evidence block by the evidence_id returned from search_evidence, including section anchor, version, equations, table cells, links, and attribution.
Input Schema
{
"type": "object",
"properties": {
"evidence_id": {
"type": "string",
"description": ""
}
},
"required": [
"evidence_id"
]
}🟢Agentic_RL_dataset_overview
Inspect ShellOps and ShellOps-Pro task counts, train/test splits, task types, published schemas, source files, license and citation.
Input Schema
{
"type": "object",
"properties": {}
}🟢Agentic_RL_search_tasks(query, partition, split, limit, offset)
Find real ShellOps CLI benchmark tasks by case-insensitive literal substring in the complete instruction, task ID or published task type. Empty query lists all tasks. Select partition 'all', 'shellops' or 'shellops_pro'; select published split 'all', 'train_src', 'train' or 'test'. Results are ordered by partition then task ID, with explicit pagination and no relevance scoring. The train subset is not double-counted.
Input Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": ""
},
"partition": {
"type": "string",
"description": "",
"default": "all"
},
"split": {
"type": "string",
"description": "",
"default": "all"
},
"limit": {
"type": "integer",
"description": "",
"default": 10
},
"offset": {
"type": "integer",
"description": ""
}
},
"required": [
"query"
]
}🟢Agentic_RL_get_task(task_id, partition)
Inspect one published ShellOps or ShellOps-Pro task by its exact task_id and partition ('shellops' or 'shellops_pro'). Returns the complete instruction, actual reward specification, published reference answer/command, file-entry metadata, pinned parquet rows and workspace asset links. File content is available at the source links. No shell execution or solution verification is performed.
Input Schema
{
"type": "object",
"properties": {
"task_id": {
"type": "string",
"description": ""
},
"partition": {
"type": "string",
"description": ""
}
},
"required": [
"task_id",
"partition"
]
}🟢Agentic_RL_list_method_facets
List exact filter values for agent RL credit granularity, supervision, value critics and evaluation settings. Each value reports its source-supported method count.
Input Schema
{
"type": "object",
"properties": {}
}🟢Agentic_RL_filter_methods(credit_granularity, required_supervision, learned_value_critic, evaluation_setting)
Filter agent RL credit-assignment methods by research conditions and return original section evidence and BibTeX. Discover accepted values with list_method_facets. Filters combine with AND; empty strings leave a facet unrestricted. Unknown critic status never matches no. Results use publication order without a relevance or quality ranking.
Input Schema
{
"type": "object",
"properties": {
"credit_granularity": {
"type": "string",
"description": ""
},
"required_supervision": {
"type": "string",
"description": ""
},
"learned_value_critic": {
"type": "string",
"description": ""
},
"evaluation_setting": {
"type": "string",
"description": ""
}
}
}Community
Evidence