PATCH /v1/agent/max-price — raise/lower ceiling (30-80). Use when tools return no_eligible_supply with cheapest_posted_cents. Does not auto-apply.
| Name | Required | Description | Default |
|---|---|---|---|
| max_price_cents_per_dollar | Yes | Buyer ceiling in cents per DIEM dollar, 30-80 |
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?
Annotations cover idempotency and non-destructiveness, and the description adds the important caveat 'Does not auto-apply,' which is genuinely useful behavioral context beyond the structured fields. It does not cover authorization or failure modes, but with annotations present the bar is lower.
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?
Every word earns its place: endpoint, action, range, usage trigger, and a behavioral caveat are packed into two short sentences with no fluff. The most important routing information is 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?
For a single-parameter setter with no output schema, the description is nearly complete: it identifies when to call it, what it changes, and a key caveat. A minor gap is that it doesn't explain how cheapest_posted_cents should map to the parameter value, but this is inferable from the schema.
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 coverage is 100%, and the schema already explains the parameter as 'Buyer ceiling in cents per DIEM dollar, 30-80.' The description repeats the range but adds no new semantic detail about the parameter itself, so the baseline 3 applies.
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?
States a specific action (PATCH endpoint to raise/lower ceiling) and the resource (/v1/agent/max-price). The 30-80 range removes ambiguity about valid scope, and the description is distinct from all sibling tool names.
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?
Gives an explicit trigger condition: 'Use when tools return no_eligible_supply with cheapest_posted_cents.' It does not name alternatives or state when not to use it, but the condition is specific enough to route an agent correctly.
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.
Several video tools are effectively duplicates: generate_video and video_queue both target POST /v1/video/queue, while get_generation_status and video_retrieve both call POST /v1/video/retrieve. The non-video tools are distinct, but these overlapping boundaries make it hard for an agent to choose the correct variant.
Tool names are uniformly snake_case and many follow a verb_noun pattern such as create_key, list_keys, and get_models. However, the video tools use an object-first video_* pattern, and names like agent_me, chat_completions, and funding_instructions break the dominant convention.
At 18 tools, the surface is on the heavy side, and the count is inflated by lower-level variants that duplicate agent-facing tools such as video_queue vs generate_video and video_retrieve vs get_generation_status. A leaner set could consolidate these while still covering account, key, model, image, and video workflows.
The set covers the account/key lifecycle, funding and price controls, model discovery, chat, image generation, and a full video quote/queue/status/retrieve/cleanup flow. It also provides request-trace recovery and capacity checks, so agents have no obvious dead ends for the stated domain.