Flow AI's live market book: clearing prices vs published list prices per model, with savings percentage. Sorted by savings. These are the prices requests actually clear at.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max rows (default 15, max 50) |
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, and the description does not mention whether the tool is read-only, has side effects, or any safety implications. It implies a passive query but does not state it explicitly, leaving behavior ambiguous.
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 relatively concise and front-loaded with the key concept (live market book, clearing vs list prices). The final sentence adds minor redundancy but does not significantly bloat the text.
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 tool without an output schema, the description covers the essential return types (prices, savings) and sorting. However, it lacks contextual details such as typical use cases or limitations, and does not leverage sibling tool distinctions for completeness.
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 single 'limit' parameter is fully described in the schema (max rows, default 15, max 50), and the tool description adds no additional meaning or context beyond that. Baseline score for full schema coverage with no extra semantic value.
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 provides live market book data including clearing prices, list prices, and savings percentages, sorted by savings. It distinguishes itself from sibling tools like list_free_models and search_models by focusing on actual clearing prices.
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 explicitly state when to use this tool versus alternatives. It lacks guidance on scenarios like real-time pricing needs or how it differs from list_free_models, leaving the agent to infer from the name.
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.