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dam2452

vastai-mcp

by dam2452

search_offers

Find rentable GPU offers on Vast.ai by filtering on GPU type, price, reliability, and more.

Instructions

Search the Vast.ai GPU marketplace for rentable machine offers.

The body is sent as-is to POST /bundles/. Top-level keys commonly include type ("ondemand" | "bid"), limit, and filter objects. Each filter maps a field name to an operator object: {eq, neq, gt, lt, gte, lte, in, notin}.

Examples: search_offers({"gpu_name": {"in": ["RTX 4090"]}, "num_gpus": {"gte": 1}, "reliability": {"gte": 0.99}, "verified": {"eq": True}, "rentable": {"eq": True}, "type": "ondemand", "limit": 5}) search_offers({"type": "bid", "gpu_ram": {"gte": 24000}, "order": [["dph_total", "asc"]], "limit": 10})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the body is passed as-is to POST /bundles/, and explains filter operators (eq, neq, gt, etc.) with two concrete examples. It does not mention rate limits, authentication, or response structure, but the read-only nature is apparent from 'search'.

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 opens with a clear purpose statement, then concisely explains the API mapping, filter syntax, and gives two illustrative examples. No redundant information; every sentence adds value.

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 the complex query syntax and a bare-bones schema, the description provides thorough guidance on constructing valid queries, including operators and representative examples. It stops short of detailing response format, but an output schema exists (not shown) to cover that, making it sufficiently complete.

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

Parameters5/5

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

Schema has a single generic 'query' object with additionalProperties: true and no property descriptions, so schema coverage is 0%. The description compensates fully by explaining top-level keys, filter object structure, operator list, and providing examples that illustrate parameter composition.

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?

Description uses a specific verb ('Search') and resource ('Vast.ai GPU marketplace for rentable machine offers'), clearly distinguishing it from siblings like search_benchmarks and search_templates. Even without a title, the purpose is unmistakable.

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?

Provides clear context (searching marketplace offers) and extensive examples showing how to formulate queries, but does not explicitly contrast with alternative tools like list_instances or get_gpu_trends. No exclusions are stated, so it meets the 'clear context, no exclusions' level.

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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