How to fund a PZERO account and authenticate MCP. MCP never accepts payment or wallet keys. Fund via Studio x402 top-up or agent HTTP signup/refill, then use Authorization Bearer pzero_…
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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 and does well by disclosing a key behavioral constraint: MCP never accepts payment or wallet keys. It also explains the intended funding mechanisms and the required Authorization Bearer format, giving useful behavioral context beyond a bare summary.
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 dense sentence that front-loads the core purpose and then delivers the essential funding and authentication details without fluff. Every clause earns its place.
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?
Given the tool has no parameters and no output schema, the description provides the necessary operational context: how to fund, how to authenticate, and what not to send. It is slightly truncated at the bearer-token example, but the guidance is sufficient for an agent to proceed.
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 tool has zero parameters and an empty input schema, so there is nothing for the description to compensate for. The funding/auth context does not relate to parameters, but no information is lost; this matches the baseline for parameterless tools.
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 covers how to fund a PZERO account and authenticate MCP, naming the resource ('PZERO account') and the goal ('fund' and 'authenticate'). It does not explicitly contrast itself with sibling tools, but its instructional nature is evident among the operational siblings.
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 usage when the agent needs funding or authentication guidance, and even lists concrete funding paths ('Studio x402 top-up or agent HTTP signup/refill'). It does not explicitly state when to use this tool versus alternatives or mention any exclusions, so the guidance is implied rather than explicit.
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.