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Inspect Chaos Core State

chaoscore_inspect
Read-onlyIdempotent

Inspect Chaos Core's live state: view registered capabilities, active policy, AI providers, audit trail, memory stats, and recent session results. Read-only introspection, no credentials exposed.

Instructions

Read-only introspection into Chaos Core's current state: registered capabilities, active policy, registered AI providers (and which one is answering reason/plan calls), the audit trail, memory store stats, and this session's most recent reasoning/plan/execution results. Never modifies anything, and never reveals credentials.

Capabilities, policy, memory, and audit are process-wide — a remote HTTP client and a local stdio client inspecting the same running server see the same values. The last_* targets and 'session' are scoped to your own MCP session.

Args:

  • target ('capabilities'|'policy'|'providers'|'memory'|'audit'|'session'|'last_reasoning'|'last_plan'|'last_execution'): what to inspect

  • response_format ('markdown' | 'json'): Output format (default: 'markdown')

Returns: Varies by target. 'capabilities': list of {name, description, risk, inputSummary, readOnly, destructive}. 'policy': the active PolicyConfig. 'providers': list of {id, displayName, configured, active}. 'memory': {recordCount, backend}. 'audit': recent {type, ts, sessionId, planId, stepId, capability, ...}[] entries. 'session': {sessionId, startedAt}. For the last_* targets: the most recent ReasoningResult / Plan / ExecutionTrace produced in this session, or null if none yet.

Examples:

  • Use when: "What can this server actually do?" -> target="capabilities"

  • Use when: "Which model is actually answering my reason/plan calls right now?" -> target="providers"

  • Use when: "Why did that step get blocked?" -> target="policy" or target="audit"

Error Handling:

  • Never errors under normal use; unknown target values are rejected by schema validation before this tool runs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYesWhich part of Chaos Core state to inspect: 'capabilities' (registry), 'policy' (active policy config), 'providers' (registered AI providers and which is active), 'memory' (record count), 'audit' (recent policy decisions + capability executions), 'session' (this MCP session's id and transport), 'last_reasoning', 'last_plan', or 'last_execution' (this session's most recent result of each stage)
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

While annotations already declare readOnlyHint=true and destructiveHint=false, the description adds critical context beyond those: it never reveals credentials, and clarifies process-wide vs session-scoped state (e.g., capabilities are global, last_* are per-session). It also discloses error behavior ('never errors under normal use'). This enriches the agent's understanding of side effects and safety without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (main purpose, Args, Returns, Examples, Error Handling). It front-loads the core purpose and scope, then provides just enough detail per target. Every sentence earns its place—examples are actionable and error handling is concise. It is comprehensive without being redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's 2 parameters with full enum coverage, no output schema, and read-only annotations, the description is remarkably complete. It explains return values for every target, clarifies session vs process-wide scope, addresses credential safety, and covers error handling. An agent has everything needed to invoke it correctly and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already fully describes both parameters with enums and descriptions (schema coverage 100%). The description's Args section restates the targets but adds value by coupling each target to its return shape in the Returns section (e.g., 'capabilities' returns {name, description, risk, ...}). This goes beyond the schema's generic phrasing and helps the agent anticipate output, though the schema already handles the basic parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a read-only introspection tool for Chaos Core's state, listing specific targets (capabilities, policy, providers, memory, audit, session, last_*). It explicitly says 'Never modifies anything' and differentiates from sibling tools (reason/plan/execute) by focusing on inspection. The verb and resource are precise, and the scope is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit examples of when to use this tool: 'What can this server actually do?' -> capabilities, 'Which model is actually answering...' -> providers, 'Why did that step get blocked?' -> policy/audit. This gives clear usage conditions and implicitly contrasts with the mutating siblings. It also states the tool is read-only, reinforcing appropriate use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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