Get a compact context bundle for planning a flow. Returns related ADRs, parallel open flows, similar done flows, forward-intents, architecture-module excerpts and drift warnings — all in one call, priority- scored, budgeted under ~5500 tokens. Call this AT THE START of planning instead of scatter...
AI agents call planning_context to retrieve information from DevFlow MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool purely retrieves and aggregates existing data (ADRs, flows, architecture excerpts, drift warnings) into a context bundle. There are no side effects, no data modifications, and no execution of commands. It is a read-only query consolidation tool with low blast radius if misused.
From the tool's definition 'Get a compact context bundle for planning a flow' and 'Returns related ADRs, parallel open flows, similar done flows, forward-intents, architecture-module excerpts and drift warnings'
Attacks that exploit this kind of access
Get a compact context bundle for planning a flow. Returns related ADRs, parallel open flows, similar done flows, forward-intents, architecture-module excerpts and drift warnings — all in one call, priority- scored, budgeted under ~5500 tokens. Call this AT THE START of planning instead of scattering wiki_search/adr_list/ flow_list calls. Use the markdown output directly as context; use the JSON sections if you want to cross-reference specific items. It is categorised as a Read tool in the DevFlow MCP Server MCP Server, which means it retrieves data without modifying state.
Register the DevFlow MCP Server MCP server in PolicyLayer and add a rule for planning_context: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches DevFlow MCP Server. Nothing to install.
planning_context is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the planning_context rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for planning_context. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
planning_context is provided by the DevFlow MCP Server MCP server (klausfreiberufler/devflow-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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