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get_capacity

GET /v1/capacity — routable DIEM and price bands before funding or spend decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_price_centsNoOptional buyer ceiling 30-80

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explicitly includes the HTTP method GET, which signals a read-only lookup, and notes that it happens before funding or spending, implying no mutation is performed. However, with no annotations and no mention of response details, authentication, or side effects, the behavioral burden is only partially met.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence with the endpoint front-loaded and no filler. It efficiently communicates the operation, the returned information, and the usage context in one line.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter GET tool, the description gives a reasonable high-level picture of when to use it and what it returns. However, there is no output schema, and terms like 'routable DIEM' are unexplained, so an agent may lack enough context to interpret the response correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage for the single optional parameter, max_price_cents, including its meaning and range. The description itself adds no parameter-specific detail, so the baseline schema coverage is sufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a GET operation on /v1/capacity and states that it returns 'routable DIEM and price bands,' which conveys the tool's resource and purpose. It does not explicitly contrast with sibling tools like funding_instructions or set_max_price, but the verb and resource are enough to distinguish it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'before funding or spend decisions' provides a clear contextual trigger for when this tool should be used. It does not name alternatives or state when not to use it, so it falls short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.3/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count3/5

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.

Completeness5/5

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.

Resources