How do I use prime-intellect-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@prime-intellect-mcp Show me available H100 pods under $2/hr" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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prime-intellect-mcp

by kvrancic

Overview Schema Source Related Servers Discussions Admin

Python

Local

prime-intellect-mcp

Let Claude Code rent, drive, and terminate Prime Intellect GPU pods on its own — with hard spend caps you control.

PyPI Python License MCP

What this is

An MCP server that connects Claude Code (or any MCP client) to your Prime Intellect account. With it, the agent can:

🔍 Find the cheapest GPU pod that matches your requirements
💸 Quote a price before committing money
🛒 Provision the pod (only after you say confirm=True)
🖥️ SSH into it (the connection string is handed to the agent's own Bash tool)
🛑 Terminate it when work is done — and warn loudly if you forget

Built for one workflow: telling Claude "rent the cheapest H100, run my training script, then kill it" and not waking up to a $400 bill.

Related MCP server: claude-colab

Install in 60 seconds

You only need this much to start renting GPUs through Claude Code:

1. Get a Prime Intellect API key

Click here to generate one → set permissions:

Scope	Level
Instances	Read and write
Availability	Read only
Billing	Read only
SSH Keys	Read only

Copy the key — it starts with pit_….

2. Add the server to Claude Code

Open ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or your project's .mcp.json, and paste:

{
  "mcpServers": {
    "prime-intellect": {
      "command": "uvx",
      "args": ["prime-intellect-mcp"],
      "env": {
        "PRIME_API_KEY": "pit_PASTE_YOURS_HERE",
        "PRIME_MAX_HOURLY_USD": "5",
        "PRIME_MAX_TOTAL_USD": "40"
      }
    }
  }
}

That's it. Restart Claude Code and ask: "What GPUs are available right now under $1/hr?"

Don't have uvx? Install it with curl -LsSf https://astral.sh/uv/install.sh | sh (or brew install uv). It's a one-liner installer for the uv package manager and you'll never have to manage a virtualenv again.

✨ Add SSH (optional, +2 min) — needed for Claude to actually run code on the pod

The server above can already provision/inspect/terminate pods. But to have Claude Code SSH into a running pod and execute commands on it, Prime Intellect needs to know your machine's public SSH key.

3. Find or generate an SSH key on your machine

ls ~/.ssh/*.pub          # if you have id_ed25519.pub or similar, you're set
# otherwise:
ssh-keygen -t ed25519 -C "you@example.com"   # press Enter through the prompts

4. Register the public key with Prime Intellect

cat ~/.ssh/id_ed25519.pub    # or whichever .pub file you have

Copy the output (one line starting with ssh-ed25519 …), then paste it into the Add SSH key form at app.primeintellect.ai/dashboard/ssh-keys.

That's it. Future pods will have your public key in authorized_keys, and Claude Code's Bash tool can SSH straight in:

ssh ubuntu@<pod-ip-from-pod_status> "nvidia-smi"

Coming in v0.2: a register_ssh_key MCP tool that does step 4 from inside Claude (no browser visit). See the issue tracker to follow along.

What Claude can now do (the 9 tools)

Tool	Use case
`list_gpu_types`	"What GPU types does Prime Intellect offer?"
`list_availability`	"Show me 1×H100 pods available under $3/hr."
`get_wallet_balance`	"How much credit do I have left?"
`pod_quote`	"Quote me a 1×A100 with 200GB disk." (no charge)
`pod_create`	"Provision the pod from that quote." (requires `confirm=True`)
`pod_list`	"Show me my running pods."
`pod_status`	"Is pod X ready? Wait until it has SSH info."
`pod_terminate`	"Kill pod X." (requires `confirm=True`)
`pod_check_runaway`	"Did I forget to terminate anything?"

Safety: nothing provisions silently

Three layers, in order:

Quote first. pod_quote returns a price + a 60-second token. No side effects. The dollar amount is now in the agent's context.
Explicit confirm. pod_create (and pod_terminate) requires confirm=True. Without it, you get a dry-run preview.
Hard env-var caps. PRIME_MAX_HOURLY_USD blocks any pod above the rate. PRIME_MAX_TOTAL_USD blocks any (rate × max_lifetime_hours) above the budget. Wallet balance is also enforced. None of these caps can be overridden by tool arguments — they're read at every call.

Defaults: PRIME_MAX_HOURLY_USD=5, PRIME_MAX_TOTAL_USD=40. Set them in your config's env block.

Every pod_create / pod_terminate is appended as JSON to ~/.prime-intellect-mcp/audit.log, so you have a complete history of what the agent did with your money.

Example prompts (paste these into Claude Code)

List the cheapest 1×H100 pods available right now. Show me the top 3 by hourly price.

Quote a 1×A100 80GB with 100GB disk, 8 vCPU, 64GB RAM. Don't provision yet —
just show me what it would cost.

I need to fine-tune a 7B model overnight. Find the cheapest 1×H100 with 200GB
disk, max $40 total budget, max 12 hours. Provision it, give me the SSH command,
and remind me to terminate when I'm done.

Check if I have any running pods I forgot about and show me their hourly cost.

Terminate pod abc123. Confirm before doing it.

Troubleshooting

Either your Claude Code config didn't pick up the env block, or you typed PRIME_API_KEY as a different variable. Verify with:

$ env | grep PRIME

inside the same shell that launches Claude Code, or paste the key directly into the JSON env block (instead of using ${PRIME_API_KEY}).

The agent picked a pod above your hard cap. Either:

Pick a cheaper GPU (list_availability with a region filter often surfaces cheaper community-priced rows), or
Raise PRIME_MAX_HOURLY_USD in your config and restart Claude Code.

Quotes live 60 seconds; the agent waited too long between pod_quote and pod_create. Just call pod_quote again — it's a no-op cost-wise.

Provisioning isn't fully done. The pod is alive but still running its install script. Call pod_status(pod_id, wait_for_ssh=True) and it will block (polling every 5s) until SSH comes up.

You haven't told Prime Intellect about your public key (or the pod was provisioned before you registered it). Fix:

Verify your pubkey is registered at app.primeintellect.ai/dashboard/ssh-keys.
Re-provision — the pod's authorized_keys is set at create time, so existing pods won't pick up keys you registered after.
If your private key has a passphrase, run ssh-add --apple-use-keychain ~/.ssh/your_key once on macOS so the agent unlocks it silently from now on.

Top up at app.primeintellect.ai/wallet and try again.

Why another one?

There's a prime-mcp-server 0.1.2 on PyPI. It's a thin proof-of-concept; this isn't a fork. Differences for unattended overnight use:

	`prime-intellect-mcp`	`prime-mcp-server` 0.1.2
Two-step quote → confirm	✅	❌
Env-var hard spend caps	✅	❌
Wallet pre-check	✅	❌
Runaway-pod detection	✅	❌
SSH handoff to agent	✅	❌
Tests	32 unit + opt-in live	None

Local development

git clone https://github.com/kvrancic/prime-intellect-mcp
cd prime-intellect-mcp
uv sync
uv run pytest -m "not live"        # 32 fast tests, no network, no spend
uv run ruff check .
uv run mypy src

Live smoke test (provisions cheapest available GPU, runs nvidia-smi, terminates; ~$0.05 spend):

PRIME_API_KEY=pit_... PRIME_LIVE_TEST=1 PRIME_LIVE_MAX_HOURLY=0.60 \
PRIME_MAX_HOURLY_USD=0.60 PRIME_MAX_TOTAL_USD=2.00 \
uv run pytest tests/test_smoke_live.py -v -s

Roadmap

v0.2 — register_ssh_key MCP tool (kill the dashboard step), Sandboxes (prime-sandboxes SDK), Environments Hub
v0.3 — Optional auto-terminate daemon (server-side enforcement of max_lifetime_hours); cost telemetry
v1.0+ — Hosted/OAuth deployment when Prime Intellect ships OAuth; submission to Anthropic connector directory

Acknowledgements

Prime Intellect for the prime Python SDK that does 90% of the work
MIT 6.8610 (Advanced NLP) for the Prime Intellect credits that made testing this possible
Anthropic for MCP
FastMCP for the framework

License

MIT — see LICENSE.

Contributing

Issues and PRs welcome. Please run uv run pytest -m "not live" and uv run ruff check . before submitting.

Available Tools

9 tools

get_wallet_balanceA

Return the current Prime Intellect wallet balance and recent billings.

Use this to estimate how long a quoted pod can run, or to check why pod_create returned an insufficient-funds error.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations provided, the description adequately discloses a read-only behavior and the return of balance and billings. There is no mention of side effects, rate limits, or auth requirements, but the tool is simple with no parameters and an output schema.

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 two sentences long with no wasted words. It front-loads the purpose immediately and follows with practical usage guidance.

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 tool has no parameters, an output schema, and no annotations, the description fully covers its functionality, including both the return value and practical use cases.

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

Parameters4/5

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

The tool has zero parameters, and the input schema coverage is 100% (vacuously). The description does not need to add parameter information, so a baseline of 4 is appropriate.

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 clearly states 'Return the current Prime Intellect wallet balance and recent billings,' identifying a specific verb and resource. It distinguishes itself from sibling tools like pod_create and pod_quote by focusing on wallet balance.

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 description provides explicit use cases: estimating pod runtime and debugging insufficient-funds errors. It lacks an explicit when-not-to-use section, but the context is clear enough for a simple getter tool.

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

list_availabilityA

List currently-available GPU pods that match the filters.

Returns the SDK's GPUAvailability rows (cloud_id, gpu_type, gpu_count, prices, disk/vcpu/memory bounds, stock_status, ...). Use this to pick a target before pod_quote, or to show the user options.

ParametersJSON Schema

Name	Required	Description
`gpu_type`	No	GPU type slug, e.g. 'H100_80GB'. Strongly recommended — the unfiltered response is large.
`gpu_count`	No	Required GPU count per pod (1, 2, 4, 8). None means any.
`regions`	No	Optional list of region slugs. None means any.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description adequately discloses that it returns GPUAvailability rows and lists fields. It implies a read-only operation and mentions the unfiltered response is large, but could add performance or reliability notes.

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 concise with three sentences: purpose, detail on returned data, and usage guidance. No superfluous words, well-structured.

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

Completeness4/5

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

Given the tool has three optional parameters and an output schema, the description covers purpose, return type, and usage context. It could elaborate on pagination or filtering behavior, but overall complete.

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

Parameters4/5

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

Schema coverage is 100% and each parameter has a description. The tool's description adds value by noting that gpu_type is strongly recommended due to large unfiltered response, which goes 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 clearly states the tool lists currently-available GPU pods matching filters, with a specific verb and resource. It distinguishes itself from siblings by mentioning its role before pod_quote or for showing options.

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 explicitly says 'Use this to pick a target before pod_quote, or to show the user options,' providing clear context. However, it does not explicitly state when not to use it or compare to alternatives.

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

list_gpu_typesA

List every GPU type Prime Intellect currently offers (e.g. "H100_80GB", "A100_80GB").

Use this when the user is vague about what they want. Pass the result into list_availability or pod_quote.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.7/5.0

Behavior4/5

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

No annotations exist, so the description must cover behavioral traits. It accurately describes a safe, read-only list operation. While it doesn't discuss data freshness or rate limits, the simplicity of the tool (no parameters, no side effects) makes the implicit behavior clear. Slight deduction for not mentioning any potential delays or consistency guarantees.

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?

Two sentences, each earning its place. The first sentence states the action and gives examples. The second provides usage guidance. No wasted words, and critical information is front-loaded.

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 tool's simplicity (no parameters, clear output described), the description is complete. The existence of an output schema means return values are fully specified. The description directly addresses the agent's need to clarify vague user requests and chain to other tools.

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

Parameters4/5

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

The input schema has no parameters, so schema description coverage is 100%. With 0 parameters, the baseline is 4. The description adds value by providing examples of GPU types, which helps agents understand the output without needing to inspect the output 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?

Description clearly states the tool lists all GPU types offered by Prime Intellect, with specific examples like 'H100_80GB' and 'A100_80GB'. It distinguishes from siblings by specifying its role in clarifying vague user requests and directing results to list_availability or pod_quote.

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

Usage Guidelines5/5

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

Explicitly states when to use the tool: 'when the user is vague about what they want.' Also provides clear next steps: 'Pass the result into list_availability or pod_quote.' This leaves no ambiguity about context and downstream usage.

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

pod_check_runawayA

Return locally-tracked pods that have run past max_lifetime_hours OR whose accumulated cost is approaching PRIME_MAX_TOTAL_USD.

Call this at the start of long-running sessions to catch forgotten pods.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool returns matching pods but does not mention whether it is read-only, side effects, rate limits, or refresh behavior. For a check tool, assuming read-only is reasonable but not explicit.

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?

Two sentences, no wasted words. The first sentence states purpose, the second provides usage guidance. Front-loaded with key information.

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

Completeness4/5

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

For a simple tool with no parameters and an output schema (not shown but exists), the description is fairly complete. It could note that the operation is read-only, but overall it covers what the tool does and when to use it.

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

Parameters4/5

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

Tool has zero parameters, baseline is 4 per instructions. Description adds context about the filtering criteria (max_lifetime_hours and cost limit) which are not parameters but clarify the tool's logic. Schema coverage is 100% due to no parameters.

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 clearly states it returns 'locally-tracked pods that have run past max_lifetime_hours OR whose accumulated cost is approaching PRIME_MAX_TOTAL_USD'. This distinguishes it from sibling tools like 'pod_list' (list all) and 'pod_status' (status of specific pod), providing a specific verb+resource combination.

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?

Explicitly advises 'Call this at the start of long-running sessions to catch forgotten pods', giving clear when-to-use context. While it doesn't exclude other scenarios, the guidance is sufficient for typical use.

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

pod_createA

Provision a Prime Intellect GPU pod (or preview the provisioning).

With confirm=False: returns a dry-run preview describing what would happen. With confirm=True: validates spend caps + quote freshness, then provisions.

The server enforces:

quote_token must be fresh (TTL 60s)
hourly_usd ≤ PRIME_MAX_HOURLY_USD
hourly_usd × max_lifetime_hours ≤ PRIME_MAX_TOTAL_USD
estimated total ≤ wallet balance

On success, the pod is recorded in local state.json so pod_check_runaway can warn about overdue pods later.

ParametersJSON Schema

Name	Required	Description
`quote_token`	Yes	Token returned by pod_quote.
`name`	Yes	Human-readable pod name.
`max_lifetime_hours`	No	Soft budget cap: hourly_usd × this must fit under PRIME_MAX_TOTAL_USD.
`confirm`	No	Required True to actually provision. False returns a dry-run preview.
`env_vars`	No	Optional env vars to inject into the pod.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, description discloses dry-run vs actual provisioning, server constraints, and side effects like recording in state.json for runaway detection.

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?

About 80 words, well-structured with bullet points, front-loaded with key action, no wasted sentences.

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?

Covers both modes, constraints, side effects. Output schema exists so return values not needed. Complete for provisioning tool.

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

Parameters4/5

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

100% schema coverage but description adds context: explains confirm's dual role, constraints on max_lifetime_hours, and how quote_token is used.

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 clearly states it provisions a GPU pod or previews provisioning, using specific verbs like 'provision' and 'preview'. It distinguishes from siblings like pod_quote and pod_terminate.

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?

Explains when to use confirm=False vs True and lists server-enforced constraints. No explicit 'when not to use' but implicit from context.

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

pod_listA

List every pod the API key can see (active + provisioning + stopped).

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description solely bears the burden. It mentions the statuses included but not any side effects, ordering, or pagination. Since an output schema exists, return value details may be covered there, but additional behavioral context (e.g., no mutations, read-only) is absent.

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, front-loaded sentence with no filler. Every word contributes meaning, making it maximally concise for its purpose.

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

Completeness4/5

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

Given zero parameters and an existing output schema, the description is largely sufficient. However, it could be slightly more complete by clarifying that it lists all visible pods without filtering (vs. pod_status for a specific pod).

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

Parameters4/5

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

No parameters exist, and schema coverage is 100% (trivially). The description adds no parameter information because none is needed. Baseline for zero parameters is 4.

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 clearly specifies the verb 'List', the resource 'pod', and the scope: 'every pod the API key can see' with explicit statuses (active, provisioning, stopped). It is distinctive from siblings like pod_status or pod_terminate.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool vs. siblings such as pod_status for a specific pod. The description only states what it does, not when it is appropriate.

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

pod_quoteA

Get a non-binding price quote + reserved provisioning payload.

Returns a quote_token (TTL=60s) that you pass to pod_create with confirm=True to actually provision. This tool has NO side effects.

The server picks the cheapest matching GPUAvailability row that satisfies the requested disk/vcpu/memory. If none matches, returns an error explaining what's available.

ParametersJSON Schema

Name	Required	Description	Default
`gpu_type`	Yes	GPU type slug, e.g. 'H100_80GB'.
`gpu_count`	No	Number of GPUs per pod (1, 2, 4, 8).
`disk_size_gb`	No	Disk size in GB.
`vcpus`	No	vCPU count.
`memory_gb`	No	Memory in GB.
`image`	No	Container image slug. Use 'ubuntu_22_cuda_12' if unsure.	ubuntu_22_cuda_12

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations provided, description carries full behavioral burden. It discloses no side effects, TTL of 60s, server picks cheapest matching row, and returns error with available options if no match. Comprehensive and honest.

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?

Four sentences with front-loaded purpose, then flow, behavior, and error case. No fluff, every sentence adds value. Very concise.

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 complexity and presence of output schema, the description covers essential aspects: return value, TTL, side-effect-free nature, selection logic, and error behavior. Complete for a quoting tool.

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 each parameter well. Description adds overall logic (cheapest matching) but no extra per-parameter meaning beyond schema defaults and examples.

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 clearly states the tool gets a non-binding price quote and reserved provisioning payload, distinguishing it from sibling tools like pod_create. It specifies the verb 'Get' and the resource, making purpose unambiguous.

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?

Description explains that the tool has no side effects and that the returned quote_token should be passed to pod_create with confirm=True to provision. It implicitly guides usage before creation, but lacks explicit when-not-to-use or alternatives.

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

pod_statusA

Get the current status (provisioning / active / failed) for a pod.

With wait_for_ssh=True, blocks (polls every 5s) until ssh_connection is available — that's when you can SSH in. Returns the SSH connection string in ssh_connection (e.g. "root@1.2.3.4 -p 22000"). Use it from your Bash tool: ssh -o StrictHostKeyChecking=no <ssh_connection> "<cmd>".

ParametersJSON Schema

Name	Required	Description
`pod_id`	Yes	The id returned by pod_create.
`wait_for_ssh`	No	If True, poll until ssh_connection is populated or timeout_s elapses.
`timeout_s`	No	Max seconds to wait when wait_for_ssh=True.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses polling behavior every 5s, blocking until SSH available, and return format for SSH connection. It does not cover rate limits or permissions but is sufficient for safe use.

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?

Description is brief (4 sentences), front-loaded with purpose, then explains the optional blocking behavior and SSH usage. Every sentence adds value with no redundancy.

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

Completeness4/5

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

Given tool complexity (polling, SSH) and presence of output schema, description adequately explains the blocking behavior and SSH string usage. Lacks details on full return object but output schema covers that.

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?

Input schema has 100% description coverage, so baseline is 3. Description adds marginal value by contextualizing SSH connection usage but essentially repeats parameter descriptions found in 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?

Description clearly states the tool gets pod status with specific statuses, and distinguishes from siblings like pod_create, pod_list, and pod_terminate by focusing on a single pod and offering SSH readiness detection.

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

Usage Guidelines3/5

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

The description implies usage for checking status and waiting for SSH, but does not explicitly state when to use versus alternatives like pod_list or pod_create, nor provides when-not-to-use guidance.

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

pod_terminateA

Destroy (terminate) a pod. Idempotent on already-deleted pods.

Without confirm=True, returns a no-op preview so you can re-read your decision.

ParametersJSON Schema

Name	Required	Description	Default
`pod_id`	Yes	The pod to destroy.
`confirm`	No	Required True to actually terminate.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description must carry behavioral disclosure. It mentions idempotency and preview behavior, but it does not disclose potential side effects, required permissions, or data loss risks. While the preview feature adds transparency, the description lacks warnings about irreversibility, making it only partially transparent.

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 two sentences long, with the first sentence concisely stating purpose and idempotency, and the second explaining the preview feature. No unnecessary words or repetitions; every sentence earns its place, making it highly efficient and front-loaded.

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

Completeness4/5

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

For a simple destructive tool with a clear output schema, the description covers purpose, idempotency, and preview behavior. However, it does not mention prerequisites (e.g., pod existence is handled by idempotency) or any contextual warnings about consequences. Slight gaps in completeness, but overall adequate given the tool's simplicity.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by clarifying the confirm parameter's preview behavior beyond the schema's 'Required True to actually terminate.' This extra context improves understanding without redundancy, justifying a score above the baseline of 3.

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 explicitly states 'Destroy (terminate) a pod' with a clear verb and resource. It also notes idempotency on already-deleted pods, adding clarity. The tool is uniquely positioned among siblings as the only destroy operation, making its purpose unambiguous.

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

Usage Guidelines3/5

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

The description explains the preview behavior with confirm=False, guiding when to preview vs execute. However, it does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare to alternatives like pod_check_runaway. The usage guidelines are implied but not fully elaborated.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

9 tool updatesv0.1.0May 5, 2026
- First observedget_wallet_balance
- First observedlist_availability
- First observedlist_gpu_types
- First observedpod_check_runaway
- First observedpod_create
- First observedpod_list
- First observedpod_quote
- First observedpod_status
- First observedpod_terminate

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored 2026-05-05 17:15 across 9 tools

Disambiguation5/5

Each tool has a unique and clearly distinct purpose, from wallet balance and GPU availability listing to pod creation, quoting, and termination. There is no overlap that could cause an agent to select the wrong tool.

Naming Consistency5/5

Tool names follow a consistent pattern: utility functions use verb_noun (e.g., get_wallet_balance, list_availability) and pod operations all start with pod_ (e.g., pod_create, pod_terminate). The naming is predictable and easily understood.

Tool Count5/5

With 9 tools covering wallet, GPU types, availability, pod lifecycle (create, list, status, quote, terminate), and runaway monitoring, the count is well-scoped for the server's purpose. Each tool earns its place with no redundancy.

Completeness5/5

The tool surface covers the full lifecycle of GPU pod management: discovering availability, quoting, creating, monitoring status, listing, terminating, and checking for runaway pods. The inclusion of wallet balance and wait-for-SSH functionality addresses common operational needs.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

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