Verified GPU compute for Blender rendering with signed receipts and proof.
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Average 3.6/5 across 8 of 8 tools scored. Lowest: 2.8/5.
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.
farpy_capabilitiesBInspectReturn FARPY compute capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral details. While 'Return' implies a read-only operation, the description does not disclose what the output contains, whether there are side effects, or any system interactions. This leaves the agent with minimal behavioral insight beyond the literal operation.
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 a single, concise sentence with no filler words. It is efficiently structured and immediately front-loaded with the action and resource.
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 the lack of an output schema, annotations, and any usage guidance, the description is too sparse to fully prepare an agent. It does not clarify the format or content of the returned capabilities, nor when the tool should be invoked relative to other FARPY operations.
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 tool has zero parameters, so the description correctly avoids redundant parameter explanations. The baseline score of 4 applies because there are no parameter semantics to elaborate.
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 uses the specific verb 'Return' and identifies the resource as 'FARPY compute capabilities', making it distinct from sibling tools like download, upload, and submit_render. The purpose is clear even without additional context.
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 provides no information about when to use this tool versus the other FARPY tools, such as whether it should be called first to determine available resources or if it has any prerequisites. There is no mention of alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
farpy_downloadAInspectReturn a short-lived authenticated FARPY artifact download URL.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | FARPY job id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds useful context by noting that the URL is 'short-lived authenticated', but it does not explain expiration duration, error conditions, or any side effects, which leaves gaps for a tool with no other safety signals.
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 a single sentence that is front-loaded and to the point. It contains no filler or redundant information, making it highly concise and well-structured.
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 tool with one parameter, no output schema, and no annotations, the description covers the core behavior and return type. It mentions key attributes (short-lived, authenticated) but could add context about when to use it (e.g., after job completion) for slightly better completeness.
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 fully documents the single parameter 'job_id' with the description 'FARPY job id'. The tool description adds no additional meaning beyond this, so with 100% schema coverage, the baseline score of 3 applies.
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 uses specific language: 'Return a short-lived authenticated FARPY artifact download URL.' This clearly identifies the verb (Return), the resource (FARPY artifact download URL), and adds distinguishing qualifiers (short-lived, authenticated) that differentiate it from sibling tools like farpy_job_status or farpy_receipt.
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 states what the tool does but gives no explicit guidance on when to use it or which alternatives to prefer. Usage is implied by the name and description, but there are no exclusions or mention of related tools, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
farpy_inspect_blendAInspectInspect a Blender .blend upload and return detected frame range and pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| upload_id | Yes | FARPY upload id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns frame range and pricing, which implies a read-only inspection, but it does not explicitly disclose whether it modifies anything, requires prior upload, or how it handles errors. This lack of detail leaves behavioral traits ambiguous.
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 a single sentence that directly states the purpose and outcome, with no unnecessary words. It is front-loaded with the verb and all information is relevant.
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 with one parameter, so the description covers the core action and output. However, it lacks workflow context such as the prerequisite of a prior upload, and with no output schema, it does not detail the structure of the returned frame range and pricing. These gaps leave it slightly incomplete.
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 describes the single parameter (upload_id) with 100% coverage, providing a baseline of 3. The description adds only that the upload is a Blender .blend file, which is also evident from the tool name, so it does not meaningfully enhance parameter understanding.
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 tool's function with a specific verb ('Inspect'), a resource ('Blender .blend upload'), and the output ('return detected frame range and pricing'). This distinguishes it from sibling tools like farpy_upload_blend and farpy_submit_render, which perform different actions.
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 gives context that this tool inspects an upload, implying it should be used after uploading and before submitting a render. However, it does not explicitly state when to use it over alternatives or provide any exclusions, so it only reaches the 'clear context, no exclusions' level rather than offering explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
farpy_job_statusAInspectReturn one FARPY render job or recent render history.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | No | Optional FARPY job id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for disclosing behavior. It only says 'Return' which implies a read operation, but it does not describe what fields are returned, how 'recent' is defined, whether errors are surfaced, or any side effects. This is minimal but not misleading.
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 a single sentence of nine words, front-loaded with the action, and contains no filler or redundant information.
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 the tool's simplicity (one optional param, no output schema), the description provides the basic purpose but lacks richer context such as expected response format, how far back history extends, or how this tool fits into the render workflow (e.g., after farpy_submit_render). It's minimally viable but leaves gaps.
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 documents the single optional parameter 'job_id' with 100% coverage. The description adds meaning by implying that providing a job_id returns that specific job, while omitting it returns recent history, which clarifies the parameter's role beyond the schema's simple 'Optional 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 uses the specific verb 'Return' and names the resource ('FARPY render job or recent render history'), clearly distinguishing from sibling tools like farpy_submit_render or farpy_download.
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 provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It simply states what it returns, without indicating e.g., use this after submitting a render job to check progress.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
farpy_prepare_uploadAInspectCreate a short-lived one-time upload URL for a local Blender .blend file. Upload the file bytes directly with HTTP PUT; do not send file bytes through model context.
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes | Filename only, ending in .blend |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It openly states that the URL is 'short-lived' and 'one-time', and that the upload must be done via HTTP PUT, warning against sending file bytes through model context. This gives the agent practical knowledge about how the tool operates, though it stops short of detailing expiration timing or auth requirements.
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 sentences long, with zero redundant words. The first sentence states the purpose; the second provides a critical usage instruction. Every word earns its place, and the structure is excellent for quick agent comprehension.
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 without an output schema, the description covers the essential workflow: create URL, then upload directly via HTTP PUT. It omits details like what the response contains or expiration specifics, but these are not strictly necessary for the agent to invoke the tool correctly. Overall, it feels complete enough for the tool's complexity.
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 describes the single parameter: 'Filename only, ending in .blend'. The description's mention of 'local Blender .blend file' adds a slight behavioral context, but it doesn't enrich the parameter semantics meaningfully beyond what the schema provides. With 100% schema coverage, 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 tool's function: 'Create a short-lived one-time upload URL for a local Blender .blend file.' The verb 'create' and specific resource 'upload URL' distinguish it from sibling tools like farpy_download or farpy_submit_render. The second sentence clarifies the intended transfer method (HTTP PUT), removing ambiguity.
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 provides a clear how-to instruction ('Upload the file bytes directly with HTTP PUT; do not send file bytes through model context') but does not explicitly compare this tool to alternatives or state when to use it vs farpy_submit_render or farpy_download. The usage context is implied by the tool's purpose, but no direct guidance on when/why to choose this tool over siblings is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
farpy_receiptCInspectRetrieve a FARPY render receipt.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | FARPY job id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It implies a read operation via 'retrieve' but provides no details about response format, potential errors, or what a receipt contains. This leaves significant behavioral ambiguity.
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 a single sentence with no wasted words, but it is under-specified for a tool with no annotations or output schema. It is concise in length but lacks structural elements like parameter context or usage notes, making it appropriately sized for a trivial tool but not fully informative.
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?
With no output schema and no annotations, the description should explain what a receipt is or what the return value looks like. It only states the action without covering expected results or error conditions, making it incomplete for effective tool selection.
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 has 100% coverage for its single parameter (job_id) with the description 'FARPY job id'. Since schema coverage is high, the baseline is 3, and the description does not add any extra meaning beyond what the schema already states.
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 tool retrieves a FARPY render receipt, which is a specific verb+resource combination. It is distinct from siblings like farpy_job_status and farpy_download, though it doesn't explicitly differentiate itself from them.
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?
No guidance is provided on when to use this tool versus alternatives like farpy_job_status or farpy_download. There is no mention of context, prerequisites, or exclusions, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
farpy_submit_renderAInspectStart a FARPY render from an inspected upload.
| Name | Required | Description | Default |
|---|---|---|---|
| frame_end | No | End frame | |
| upload_id | Yes | FARPY upload id | |
| frame_start | No | Start frame |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only says 'Start a FARPY render' without mentioning whether the operation is asynchronous, whether it returns a job ID, any side effects, or cost implications. This leaves significant behavioral ambiguity.
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 a single, minimal sentence that is immediately understandable and contains no redundant words. It is front-loaded with the key action and resource.
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 description is too terse for the context. With no output schema and no annotations, it fails to explain what happens after submission (e.g., does it return a job ID? how to track progress?). It also doesn't mention how frame_start/frame_end relate to the render behavior. This is insufficient for an agent to know how to proceed.
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 provides descriptions for all three parameters (upload_id, frame_start, frame_end), giving 100% coverage. The description adds no extra parameter semantics, so the baseline score of 3 applies.
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 'Start' and the resource 'FARPY render', with the qualifier 'from an inspected upload' adding essential context. It distinguishes itself from sibling tools like farpy_upload_blend (uploading) and farpy_inspect_blend (inspecting) by specifying the render-starting action.
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 'from an inspected upload' implies the tool must be used after farpy_inspect_blend, providing clear context. However, it does not explicitly name alternatives or state when not to use the tool, though the implied prerequisite is reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
farpy_walletAInspectReturn FARPY wallet balance.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 implies a read-only operation ('Return'), but does not disclose any behavioral traits such as authentication requirements, network dependencies, error behavior, or whether the balance is real-time or cached. The description is not misleading but lacks depth.
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 a single concise sentence with no fluff. Every word is meaningful and directly conveys the tool's purpose.
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 the tool's simplicity - no parameters, no output schema, and no complex behavior - the description is sufficient for a basic understanding. It could mention what the returned balance represents (e.g., in FARPY tokens) or return format, but this is not critical for a zero-argument read tool.
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?
There are no parameters, so the description does not need to add parameter-level detail. The schema coverage is 100% trivially, and the baseline for zero parameters is 4.
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 ('Return') and the resource ('FARPY wallet balance'), making it specific and unambiguous. It is distinct from sibling tools like farpy_submit_render or farpy_job_status, which serve different purposes.
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?
No explicit guidance on when to use this tool or when to use alternatives is provided. The intended use is implied by the name and description, but the description does not mention any context, exclusions, or relationships to sibling tools.
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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