antigravity-m2m-computational-factory
Server Details
Hardware-aware spatial kinematics solvers, block-wise asymmetric INT8 tensor quantisation runtimes, and high-entropy AST boundary fuzzing routers optimised for autonomous AI agent workflows.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.2/5 across 3 of 3 tools scored.
Each tool targets a clearly distinct operation: kinematics (physics), boundary extraction (validation), and quantization (compression). No overlap in purpose.
All tools follow a consistent verb_noun pattern using snake_case, e.g., compute_kinematics, fuzz_boundaries, quantize_tensors.
With 3 tools, the count is well-scoped for a specialized computational factory server, covering diverse but focused operations.
The domain appears to be computational physics/ML support, but missing common operations like matrix inversion or model inference, though core utilities are present.
Available Tools
3 toolscompute_kinematicsBInspect
Calculates joint-space mass matrices M(q) and analytical spatial derivatives using parallel geometric NumPy fallback solvers.
| Name | Required | Description | Default |
|---|---|---|---|
| joint_angles | Yes | Vector of target spatial joint positions. | |
| robot_definition | Yes | JSON-serialized link geometries, inertial metrics, and reference offsets. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions 'parallel geometric NumPy fallback solvers' but does not disclose whether the tool is read-only, side effects, or constraints. Critical behavioral traits are missing.
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 wasted words. It is efficiently front-loaded with the key action and result.
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?
Without an output schema, the description does not explain the return format or structure. It omits prerequisites like valid JSON and matching dimensions, making it incomplete for a tool with this 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?
Schema coverage is 100% with clear parameter descriptions. The tool description adds no extra parameter-level meaning, meeting the baseline.
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 it calculates joint-space mass matrices and analytical spatial derivatives, using a specific verb and resource. The sibling tools are unrelated, so no need for differentiation.
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 on when to use this tool versus alternatives or when not to use it. The description lacks context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fuzz_boundariesCInspect
Extracts mathematical array limits, comparison boundaries, and numeric edge constraints to validate memory limits.
| Name | Required | Description | Default |
|---|---|---|---|
| source_code | Yes | Raw string of programmatic mathematical syntax loop structures. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It mentions extracting limits for memory validation but does not describe side effects, performance, error handling, or output format. This is insufficient for understanding behavior.
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, which is concise but not well-structured. It front-loads the action but lacks further detail; the sentence earns its place but barely adds value.
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 potentially complex task and the lack of output schema or annotations, the description is too minimal. It does not explain what is returned (e.g., format, structure) or how the extraction connects to memory validation, leaving significant 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?
Schema coverage is 100% with one parameter described as 'Raw string of programmatic mathematical syntax loop structures.' The description adds some context by clarifying the extracted boundaries, but it does not significantly enhance parameter understanding beyond 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 tool extracts mathematical array limits and boundary conditions to validate memory limits, providing a specific verb and resource. It distinguishes from sibling tools 'compute_kinematics' and 'quantize_tensors', which are unrelated.
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 on when to use this tool versus alternatives, no mention of prerequisites or context. The description lacks any usage recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quantize_tensorsAInspect
Executes hardware-aware piecewise asymmetric INT8 quantization to compress dense floating-point matrix weights arrays.
| Name | Required | Description | Default |
|---|---|---|---|
| block_size | No | Size of distinct block scaling segments. | |
| tensor_data | Yes | Flattened float32 dense input array weights. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the technical nature of the operation (hardware-aware, piecewise, asymmetric) but does not disclose side effects, required permissions, or performance characteristics. With no annotations, the description carries the full burden but is limited to technical detail.
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, well-formed sentence that directly states the tool's function without extraneous 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?
Given the absence of an output schema, the description should ideally clarify what the tool returns, but it only describes the input process. The 2-parameter schema is fully described, but the output remains obscure.
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 coverage is 100%, so the baseline is 3. The description does not add new meaning to the parameters beyond what the schema already provides (tensor_data as flattened float32 array, block_size with default).
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 it executes hardware-aware piecewise asymmetric INT8 quantization on dense floating-point matrix weights arrays. The verb 'executes' and specific resource distinguish it from unrelated sibling tools like compute_kinematics and fuzz_boundaries.
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, such as compute_kinematics or fuzz_boundaries. The description does not mention prerequisites, constraints, or typical use cases.
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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