Skip to main content
Glama

cpu_get_lot_terms

Check live Hub and resource listing terms before committing—min/max units, current lot counts, evicted remainders, and a canList verdict with blockers—so you don't spend on refused listings.

Instructions

Read the live terms for listing one resource on one Hub, before you spend anything (needs a session — cpu_authenticate first). Returns the effective minimum and maximum units one new lot may hold there, how many live lots you already hold for that Hub and resource against your limit (delivering, open and evicted ones all count), how many evicted remainders you still owe a return on at that Hub, and a plain canList verdict with the blockers behind it. Every number is read from the Trade contract itself for this exact Hub and resource. cpu_create_lot checks the same terms again before it spends, so a listing these terms refuse never costs you an approval or gas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hubTokenIdYesThe Hub cell token id you want to list on.
resourceIdYesResource type id you want to list there.

Schema Changelog

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

  1. Addedv0.10.0

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so well. It explicitly says the operation 'Reads' data, notes that values are sourced from the Trade contract for the exact Hub/resource, and clarifies that a refused listing never costs approval or gas. It also discloses the counting semantics for live lots and evicted remainders.

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 long but every sentence earns its place: purpose, session requirement, return contents, data source, and cost safety. The key purpose is front-loaded in the first clause, and there is no filler or repetition of schema fields.

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

Completeness5/5

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

Given no output schema and no annotations, the description is remarkably complete. It covers prerequisites, the conceptual return payload, the authoritative data source, and the safety guarantee around gas/approvals. An agent has enough context to invoke the tool correctly and interpret its results without additional documentation.

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?

Schema description coverage is 100%, so the schema already documents hubTokenId as the Hub cell token id and resourceId as the resource type id. The description reinforces that the tool targets one exact Hub and resource but adds no meaningful parameter behavior beyond the schema.

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

Purpose5/5

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

The description opens with a specific verb and resource: 'Read the live terms for listing one resource on one Hub.' It also enumerates the precise outputs (min/max units, lots held, evicted remainders, canList verdict), making the tool's purpose unmistakable and distinguishable from siblings like cpu_create_lot.

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?

It clearly states when to use the tool: before spending anything to list, and it notes the session prerequisite (cpu_authenticate first). It also points to cpu_create_lot as the action that re-checks terms before spending. However, it does not explicitly address when not to use this tool or contrast it with other read/quote siblings.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/projectcpu/project-cpu-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server