Read the FLOCORE work-queue summary: open items by status, depth, oldest-queued age, awaiting-human, dead-letter, stuck. Optionally scoped to a tenant.
| Name | Required | Description | Default |
|---|---|---|---|
| tenant_slug | No |
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?
No annotations are provided, so the description carries the full burden of signaling behavior. It makes the read-only nature explicit with 'Read' and discloses the optional tenant scoping plus the specific data categories returned. It does not cover auth or rate limits, but for a simple queue-summary read this is adequate.
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 core purpose is front-loaded, and the list of summary contents is compact yet informative.
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?
For a tool with one optional parameter and no output schema, the description covers the key context: it is a read, it reports queue health metrics, and it can be tenant-scoped. Minor gaps like exact return formatting or behavior for an unknown tenant are acceptable given the tool's low complexity.
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 has no descriptions and 0% coverage, so the description's 'Optionally scoped to a tenant' adds genuine meaning beyond the bare tenant_slug name and string type. It clarifies that the parameter is optional and acts as a scope filter, though it does not explain slug format or default behavior.
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 uses a specific verb ('Read') and names the resource ('FLOCORE work-queue summary'), then enumerates exactly what the summary contains: status, depth, oldest-queued age, awaiting-human, dead-letter, stuck. This clearly distinguishes it from sibling tools like flocore_list_tickets or flocore_orch_board.
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 use whenever an aggregate queue-summary view is needed, but it never states when to prefer this over alternatives or when not to use it. It also does not explicitly mention sibling tools such as flocore_list_tickets, so routing to the right tool relies on inference rather than explicit guidance.
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.