Verified GPU compute for Blender rendering with signed receipts and proof.
Return a ZIP download URL for a completed job. Never returns ZIP bytes. Delivery tokens are not logged.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | FARPY job id. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well: it states the return is a URL, explicitly says it never returns ZIP bytes, and discloses that delivery tokens are not logged. These are non-obvious behavioral traits that an agent needs to know before invoking the tool.
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 very short, front-loaded with the primary purpose, and every sentence adds distinct value: what it returns, what it never returns, and a logging behavior. There is no filler or repetition.
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?
For a simple one-parameter tool with a complete input schema and an output schema, the description provides all essential context: the return type, the completion precondition, a negative contract, and a privacy-related behavior. Nothing critical is missing for correct selection and invocation.
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 input schema already fully documents the single 'job_id' parameter, so the baseline is 3. The description adds the meaningful constraint that the job must be completed, which sharpens the parameter semantics beyond the schema's generic 'FARPY job id'.
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 names a specific action ('Return a ZIP download URL') and resource ('a completed job'), making the tool's purpose unambiguous. It is clearly distinct from the sibling tools by the download action and from any tool that would return bytes.
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 phrase 'for a completed job' gives a clear condition for when this tool should be used, and the warning that it never returns ZIP bytes helps prevent misuse. It does not explicitly name alternative tools or state when not to use it, but the usage context is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Inspect a Blender .blend without spending. Creates upload_id and an open locked quote.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Absolute path to a local .blend file. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does well by stating that no money is spent, that an upload_id is created, and that a quote is opened and locked. This reveals cost behavior and side effects beyond what the tool name alone implies.
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 only two sentences, front-loads the core purpose, and every clause adds information. There is no filler, repetition of the tool name, or redundant schema content.
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?
This is a low-complexity tool with one fully documented parameter and an output schema, so the description does not need to explain return values. It covers the main purpose and the key side effect, though the notion of an 'open locked quote' is jargon that could use a bit more context.
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 schema already provides 100% coverage of the single parameter, file_path, with a clear description. The tool description adds no additional parameter-level detail, so the baseline of 3 is appropriate.
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 verb 'Inspect' and the resource 'a Blender .blend', and adds concrete side effects: creating an upload_id and an open locked quote. It does not explicitly name or differentiate from siblings like farpy_start or farpy_quote, so it stops short of a 5.
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 phrase 'without spending' implies this tool is the free inspection step before a paid operation, and creating a quote suggests it precedes farpy_quote or farpy_start. However, it never explicitly says when to use this tool instead of a sibling or names alternative tools, so usage guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a locked quote for a frame sub-range. Does not spend.
| Name | Required | Description | Default |
|---|---|---|---|
| frame_end | Yes | Last frame, inclusive. | |
| upload_id | Yes | Upload id from farpy_inspect. | |
| frame_step | No | Frame step. | |
| frame_start | Yes | First frame, inclusive. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does disclose the important side-effect boundary: 'Does not spend.' It does not explain what 'locked' means behaviorally, such as whether the quote expires, reserves resources, or is immutable, leaving some uncertainty.
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 two short sentences with no filler. The primary purpose is front-loaded and the critical behavioral constraint, 'Does not spend,' is stated separately and clearly.
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?
For a relatively simple tool with a complete input schema and an output schema present, the description covers the essential purpose and safety-relevant behavior. It is slightly incomplete around what 'locked' means and when to prefer this over a spending operation, but not critically so.
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?
Schema description coverage is 100%, so the schema already documents all four parameters. The description adds only the high-level 'frame sub-range' context and does not add deeper parameter semantics beyond what the schema already provides.
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 states a specific action: creating a 'locked quote' for a 'frame sub-range', which clearly names both verb and resource. It also distinguishes itself from siblings by noting it does not spend, which is especially relevant next to farpy_start.
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 through the phrase 'Does not spend,' signaling that this is for quote-only operations rather than executing work. However, it does not explicitly state when to use this tool instead of farpy_start or farpy_download, nor does it name any alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return a safe receipt subset. SHA-256 is byte identity, not artistic review.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | FARPY job id. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden, and it does add one useful trait: the result attests to byte identity and is not an artistic review. It does not, however, explain what makes the subset 'safe', whether the operation is read-only, or what data is omitted.
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 only two short sentences and is front-loaded with the return action. The second sentence is cryptic but compact, and both sentences contribute meaning, so there is little waste.
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?
Although the output schema covers return values and there is only one parameter, the description leaves the core concept of a 'safe receipt subset' unexplained and gives no guidance on choosing this tool among its five siblings. An agent could call it, but would struggle to know what it will receive or why it is safe.
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 only parameter, job_id, already has a schema description ('FARPY job id.'), so schema coverage is 100%. The tool description adds no details about how job_id is validated or used beyond receiving the receipt, so the baseline of 3 is appropriate.
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 names an operation ('Return') and a resource ('safe receipt subset'), so it is not a tautology, but 'safe' and 'subset' are undefined domain jargon. The SHA-256 sentence hints at the receipt's role but does not clearly distinguish this from farpy_inspect or farpy_status.
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?
'SHA-256 is byte identity, not artistic review' implies this tool is for cryptographic/byte-level receipt verification rather than human quality review, which is an implicit when-not. However, it does not name sibling alternatives or state explicit conditions for choosing farpy_receipt over farpy_status or farpy_download.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ONLY spend tool. Reserves wallet cents for the locked quote_id.
| Name | Required | Description | Default |
|---|---|---|---|
| quote_id | Yes | Locked quote id. | |
| upload_id | Yes | Upload id from farpy_inspect. | |
| webhook_url | No | Optional HTTPS webhook for render.completed|failed|cancelled. Omit to poll with farpy_status. | |
| legal_acceptance | Yes | Must be exactly FARPY_LEGAL_V1. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing side effects. It does state that it spends/reserves wallet cents, but omits critical behavioral traits such as irreversibility, legal acceptance requirements, and lifecycle events (completed/failed/cancelled).
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 extremely concise, front-loaded, and free of filler. Every word contributes, though the brevity leaves some contextual gaps.
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 this is a financial/spending tool with no annotations, the description is incomplete. It does not explain the overall workflow or prerequisites, leaving the agent dependent on the schema and sibling tool names to infer correct usage.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds only the 'wallet cents' nuance and does not meaningfully extend the parameter meanings already provided in the schema.
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 action ('spend') and the resource ('wallet cents') for a specific context ('locked quote_id'). The phrase 'ONLY spend tool' distinguishes it from sibling tools, though it does not name an alternative explicitly.
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?
There is no explicit guidance on when to use this tool versus alternatives. 'ONLY spend tool' implies the context, but prerequisites like obtaining a locked quote or upload via farpy_inspect are left to the parameter descriptions rather than the tool description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read normalized job state and polling guidance. No worker, provider, or progress-percent fields.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | FARPY job id. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose that this is a read operation and explicitly lists absent fields, which is useful. Still, it does not explain what 'normalized' means, how polling guidance is expressed, or what error/edge behavior might occur.
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?
Two concise sentences deliver the core action, the output scope, and a key limitation with no filler. The main verb and resource are front-loaded, and every sentence 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?
The tool is simple: one required, well-documented parameter, an output schema is present, and the description covers purpose and limitations. It could more explicitly connect to farpy_start for job lifecycle context, but nothing needed to invoke it correctly is missing.
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?
Schema description coverage is 100%, and the single required parameter job_id is already documented as 'FARPY job id.' The description adds no additional parameter-level meaning, so the baseline 3 is appropriate.
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 states a specific verb and resource: 'Read normalized job state and polling guidance.' The negative clause 'No worker, provider, or progress-percent fields' further clarifies scope. It does not explicitly name a sibling, but the purpose is unambiguous.
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?
'Polling guidance' implies this tool should be used when checking job progression, and the excluded fields suggest when it is not appropriate. However, there is no explicit when-to-use or when-not-to-use guidance, nor any named alternative such as farpy_inspect.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Add one secure layer between your agents and this server.
Each tool targets a distinct phase of the rendering workflow: capabilities, upload, inspect, submit, status, download, receipt, and wallet. There is no overlap in purpose, making misselection unlikely.
All tools share the farpy_ prefix and snake_case, but some use verb_noun (inspect_blend, submit_render) while others are noun-only (capabilities, receipt, wallet). This is a minor inconsistency but the pattern is still readable.
Eight tools is well within the optimal range for a focused cloud rendering service, covering the full lifecycle without unnecessary bloat. Each tool earns its place.
The toolkit covers the entire render pipeline from upload to payment receipt, with no obvious dead ends. A cancel operation is the only notable gap, but it is not integral to the core workflow.