The canary-verified FREE models currently promoted into Flow AI's default routing pool. Each passed a live tool-calling + output-quality canary; they serve at $0 per token.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 transparency burden, but it does not describe whether this is read-only, what the response contains, or any behavioral expectations beyond the models being canary-verified and free. The $0/token note is a property of the models, not a clear execution behavior.
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?
Two sentences, both focused on the curated list and why the models are trustworthy. The phrasing could be slightly more direct—it reads as a definition rather than an action-oriented command—but no words are wasted.
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?
This is a low-complexity, zero-parameter tool, so the description covers enough to invoke it. However, since there is neither an output schema nor annotations, the description should have stated what kind of data the tool returns; that is missing.
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?
There are zero parameters, so the baseline is 4. The description does not need to add parameter semantics, and the 100% schema coverage confirms nothing is left undocumented.
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 names the resource (canary-verified FREE models in Flow AI's default routing pool) and distinguishes it from sibling tools like search_models and get_live_prices. It lacks an explicit verb in the prose, with "list" appearing only in the tool name, which keeps it from being a full 5.
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 the tool is for retrieving the current set of free, promoted models, and the context about being the default routing pool is useful. However, it gives no explicit when-to-use guidance, no when-not-to-use conditions, and does not mention alternatives.
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.
Each tool has a clearly distinct purpose: the bus_* tools cover specific messaging operations (send, receive, reply, ack, check, inspect, signup, directory) with no overlap, and the Flow AI tools cover distinct queries/actions (about, prices, free models, search, delegate, convene). No two tools could be confused.
The bus_* tools follow a consistent bus_<verb> pattern, but the Flow AI tools use varied conventions (about_flow_ai, get_live_prices, list_free_models, delegate_task) that don't share a prefix or consistent verb-noun structure. This mix is readable but not uniform across the whole set.
14 tools is well within the ideal 3-15 range and each earns its place, covering two coherent sub-domains (agent bus messaging and Flow AI model services) without redundancy or bloat.
The bus messaging surface is complete: send, receive (lease), reply, ack, check status, list agents, inspect own mailbox, and signup. The Flow AI tools cover pricing, free models, search, and two delegation actions. Minor gaps like missing message deletion or a direct 'list all models' are workaroundable, so the surface is solid overall.