Raise an operational ticket. WORLD-AFFECTING: this is gated on a human approval and will return APPROVAL_REQUIRED rather than executing.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| description | No | ||
| tenant_slug | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly reveals that this is WORLD-AFFECTING, requires human approval, and returns APPROVAL_REQUIRED rather than executing. This is strong, non-obvious behavioral information.
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 two short sentences with no filler. The purpose is stated first, and the critical world-affecting approval warning is immediately 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?
The description covers the core purpose and a critical behavioral gate, but with no output schema, no annotations, and no parameter documentation, it leaves significant gaps: what tenant_slug should be, what a successful call returns, and how the approval flow resolves. These omissions make it incomplete for reliable invocation.
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?
Schema description coverage is 0%, and the description does not explain the meaning of tenant_slug, title, or description. It adds no parameter-level value beyond the raw schema, so an agent cannot confidently know what values to supply.
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 states a clear verb and resource: 'Raise an operational ticket.' It is immediately distinguishable from the sibling read/list tools such as flocore_list_tickets, so an agent can tell what this tool does at a glance.
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 gives no explicit guidance about when to use this tool versus alternatives, and it never mentions sibling tools like flocore_list_tickets or flocore_list_tenants. The approval warning is useful cautionary context, but it does not provide selection criteria.
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 target distinct domains, but flocore_mesh_lanes and flocore_orch_board both expose mesh lane liveness, and flocore_list_tickets overlaps with flocore_queue_summary in surfacing work items by status. The descriptions help clarify the difference, but an agent could still hesitate when selecting between them.
All tools share the flocore_ prefix and use snake_case, which is good, but the pattern is inconsistent: some use verb_noun (create_ticket, list_tenants, list_tickets) while others are bare noun phrases (mesh_lanes, ontology_entities, orch_board, queue_summary) or a bare verb (recall). The convention is readable but not uniform.
Eight tools is a well-scoped size for a governed operational server. Each tool represents a meaningful capability, and there is no obvious bloat or overly thin coverage.
The read side is strong: tenants, tickets, mesh state, ontology, orchestration board, queue summary, and memory recall are all covered. However, the ticket lifecycle is incomplete with only create and list, lacking update, resolve, or single-ticket detail operations, which creates a notable gap for operational workflows.