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nucleus

by io.github.eidetic-works

Feature Flags

nucleus_features

Feature tracking, proof generation & MCP server mounting.

Actions: add - Add a feature. params: {product, name, description, source, version, how_to_test, expected_result, status?, tags?} list - List features. params: {product?, status?, tag?} get - Get feature by ID. params: {feature_id} update - Update feature fields. params: {feature_id, status?, description?, version?} validate - Mark feature validated. params: {feature_id, result} search - Search features. params: {query} mount_server - Mount external MCP server. params: {name, command, args?} thanos_snap - Trigger Instance Fractal Aggregation unmount_server - Unmount MCP server. params: {server_id} list_mounted - List mounted MCP servers discover_tools - Discover tools from mounted servers. params: {server_id?} invoke_tool - Invoke tool on mounted server. params: {server_id, tool_name, arguments?} traverse_mount - Recursively mount downstream servers. params: {root_mount_id} generate_proof - Generate proof document. params: {feature_id, thinking?, deployed_url?, files_changed?, risk_level?, rollback_time?} get_proof - Get proof for a feature. params: {feature_id} list_proofs - List all proof documents

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
paramsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

B3.1/5.0
Behavior3/5

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

Annotations indicate not read-only and not destructive, but the description adds little behavioral context beyond the action names. For example, it doesn't explain side effects of mounting servers or that actions like 'thanos_snap' might be experimental. The description is neutral and does not contradict annotations, but additional details would improve transparency.

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

Conciseness3/5

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

The description is front-loaded with a concise summary line, but the subsequent list of 14 actions is lengthy and somewhat repetitive. It could be more structured (e.g., grouping actions by category). The use of inline parameter lists makes it dense but not optimally scannable.

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

Completeness2/5

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

Despite mentions of an output schema in context, the description does not explain what actions return (e.g., list features returns an array, generate_proof returns a document). It also omits behavioral details like pagination, error handling, or rate limits. For a tool with many sub-actions, this is incomplete.

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 is generic (action + params object) with 0% coverage, so the description bears full responsibility for parameter documentation. The description lists parameters for each action, often with optional markers ('?'), adding significant meaning. However, the format is informal and some parameters lack clarity (e.g., 'result' in validate).

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

Purpose4/5

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

The description clearly states the tool handles 'Feature tracking, proof generation & MCP server mounting' and lists specific actions. The purpose is well-defined, distinguishing it from sibling tools that likely focus on other domains. However, the title 'Feature Flags' is slightly narrow given the server mounting capabilities.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus sibling tools like 'nucleus_agents' or 'nucleus_infra'. There is no mention of prerequisites, context, or when not to use it. The description simply lists actions without usage recommendations.

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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TDQS

B3.3/5.0
Disambiguation4/5

Tools are grouped by domain with detailed descriptions, but some overlap exists (e.g., multiple relay-related tools like nucleus_relay, nucleus_relay_subscribe, nucleus_next_message). Overall, most tools have distinct purposes, and descriptions help disambiguate.

Naming Consistency5/5

All tool names follow the 'nucleus_' prefix with a consistent noun-like second part (e.g., nucleus_agents, nucleus_audit, nucleus_delegate). Even compound names like nucleus_lane_feedback maintain the pattern, with no mixing of conventions.

Tool Count3/5

With 28 tools, the count is on the higher end but still justifiable given the broad scope of an agent operating system. The tools cover many necessary functions, though the set could be slightly trimmed for focus.

Completeness4/5

The tool surface covers most aspects of an agent OS: agents, audit, delegation, memory, features, federation, governance, infra, lanes, messaging, orchestration, plans, relay, routing, sessions, slots, sync, tasks, telemetry. Minor gaps like a dedicated config tool are absent but not critical.

Resources