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Agent runtime and MCP research
Examines agent execution loops, persistence, tool protocols and permission boundaries behind framework demonstrations and marketing claims.
ai-agent-runtime-mcp-research
Agents using this skill
When is it useful?
Use when deciding whether an agent framework or MCP server fits your existing orchestration system.
What does it help you do?
- Traces checkpoints, cancellation, output validation and limits on repeated execution.
- Inspects transport handling, tool schemas, output size and credential boundaries.
- Identifies useful context or retrieval patterns that can be adopted independently.
Try asking this
Inspect this MCP agent repository and explain restart recovery, unrestricted tools and which useful patterns we can reuse. Produce a code-linked comparison of recovery behavior and tool restrictions. Mark claims that are documented but unsupported by an executable test.
Detailed documentation
Read the skill’s original instructions, examples and checks in their authored language.
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AI Agent, Runtime & MCP Ecosystem Research
AI and agent repositories are especially prone to "prompt-demo hype" — projects that look magical in a 30-second video but consist of brittle regexes, unbounded token loops, or hardcoded happy paths. Always dissect the runtime mechanics underneath.
Workflow
- Dissect the agent execution loop and state model:
- Locate the actual turn/step loop in source code and verify:
- State persistence & resumability: Is run state checkpointed durably between tool calls, or held only in volatile in-memory variables?
- Termination & loop guards: How are max turns, token budgets, repeated identical tool errors, and
AbortSignalcancellations enforced? - Structured output & tool parsing: Does it use native provider tool-use / structured-output schemas with validation and repair, or fragile string splitting?
- Locate the actual turn/step loop in source code and verify:
- Evaluate Model Context Protocol (MCP) servers and integrations:
- Inspect MCP implementations across protocol and security dimensions:
- Transport support:
stdiosubprocess lifecycle (clean shutdown, stderr logging vs. stdout JSON-RPC frame corruption),SSE, or Streamable HTTP. - Tool & resource schema quality: Are input JSON Schemas strict, descriptive, and bounded in output size, or do they dump multi-megabyte raw payloads into the model context?
- Permission & secret boundaries: How are credentials passed? Does the MCP server expose unrestricted filesystem, shell, or network access without path/command allowlists?
- Transport support:
- Inspect MCP implementations across protocol and security dimensions:
- Analyze memory, RAG, and context-management architectures:
- Trace how context is assembled on each turn:
- Prompt cache friendliness: Does the system preserve a stable prefix (system prompt + tool definitions + frozen history) so Anthropic/OpenAI/Gemini prompt caching works, or does it inject dynamic timestamps/UUIDs at the top of every prompt?
- Compaction & retrieval: How are long tool outputs truncated, summarized, or paged? What storage engine and embedding/reranking dependencies are required?
- Trace how context is assembled on each turn:
- Extract reusable patterns over bloated wrappers:
- Many "agent frameworks" wrap 50 lines of provider SDK calls in 15 layers of abstract classes. When a project's core value is a clever prompt structure, compaction heuristic, or tool schema, recommend extracting the pattern or adapting the minimal logic rather than adopting a rigid framework that fights our orchestrator.
Included files 1
SKILL.md