FULL Closed-Loop Autonomous Operations Protocol in one call. Server-side AI analyzes the issue, generates remediation tasks, creates a task list, and dispatches to the worker — all without human intervention. This executes Patent Claim 7: autonomous operations from issue detection through physical-world instruction delivery.
| Name | Required | Description | Default |
|---|---|---|---|
| issue_id | Yes | Issue ID to auto-resolve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond the annotations by detailing the server-side AI analysis, creation of a task list, and dispatch to a worker, including physical-world instruction delivery. This complements the annotations (readOnlyHint=false, destructiveHint=false) and provides a clear picture of the multi-step side effects without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences and not overly long, but it contains marketing-like phrasing such as 'FULL Closed-Loop Autonomous Operations Protocol' and a patent claim reference that may be unnecessary for practical understanding. The core behavior is present but could be stated more directly and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, yet the description does not explain what the tool returns or the outcome after dispatch, such as a confirmation, task list, or status update. It also omits prerequisites like the issue being in a resolvable state, leaving the agent without a full understanding of result handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers issue_id with 100% description coverage, so the schema documents the parameter adequately. The description does not add extra meaning about the parameter format, source, or constraints, leaving the baseline score of 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly indicates the tool performs a full autonomous resolution workflow: analyzing the issue, generating remediation tasks, creating a task list, and dispatching to a worker. This goes beyond simply saying 'auto-resolve' and distinguishes it from sibling tools like tascan_analyze_issue or tascan_add_tasks by emphasizing the complete closed-loop behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when full autonomous resolution without human intervention is desired, but it does not explicitly state when to use this tool versus alternatives. It lacks exclusions or a direct comparison with sibling tools, such as suggesting tascan_analyze_issue for only analysis or tascan_dispatch_instruction for only dispatch.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map to a distinct resource+action pair, and the descriptions clearly separate entities like tasks, subtasks, workers, invoices, zones, assets, and reports. A few close pairs (get_report vs generate_report, dispatch_instruction vs dispatch_to_agent, analyze_issue vs auto_resolve) require careful reading, but the descriptions are detailed enough to disambiguate them.
The vast majority follow a tascan_verb_noun pattern with consistent create/get/list/update/delete verbs. Minor deviations like condition_history, server_info, zone_compliance, and one-word find slightly break the otherwise predictable pattern.
At 69 tools, this is far beyond what an agent can efficiently consider, and it bundles several distinct domains into one MCP surface. Even if each tool is individually useful, the combined set is an extreme mismatch for a coherent tool interface.
The surface covers nearly the full lifecycle for projects, events, tasks, subtasks, workers, reports, invoices, zones, assets, issues, and communications. Minor gaps exist (no explicit asset updates/decommissioning, no cancel_invite, no delete_zone), but the main workflows have no dead ends.
