Search Flow AI's live model catalog by name/provider/family. Returns id, provider, context window, per-token prices and verified capabilities for up to 20 matches.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | substring to match against model id/provider/family |
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, the description carries the full burden. It adds useful behavioral context such as 'live' (real-time) and a limit of up to 20 matches, but it does not disclose error behavior, authentication needs, or whether the operation is read-only. This is adequate but not comprehensive.
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 a single, well-structured sentence that front-loads the core purpose and then enumerates the return fields and match limit. There is no redundant wording, making it highly efficient.
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 simple one-parameter tool with no output schema, the description provides the key return fields and the match limit, covering essential invocation details. It omits error handling and sorting/pagination nuances, but these are minor for a straightforward search with a fixed result cap.
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 schema already fully describes the 'query' parameter as a substring to match against model id/provider/family, and the tool description repeats this without adding new meaning. Since schema description coverage is 100%, the description adds no additional semantics beyond the baseline.
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 states the tool searches Flow AI's live model catalog by name/provider/family and specifies the returned fields. It is specific and not a tautology, but it does not explicitly differentiate from sibling tools like get_live_prices or list_free_models, though the scope is evident.
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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The usage context is only implied through the stated purpose, leaving the agent to infer when this search is appropriate compared to price or free-model lookups.
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.