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liangjunglj-cpu

Almond MCP

run_gh_definition

Run an audited Grasshopper capsule in Rhino by providing inputs validated against its manifest, returning computed outputs, warnings, and analysis method.

Instructions

Runs an audited Grasshopper capsule (GH definition + typed contract) through the Rhino bridge and returns its declared outputs.

Workflow: call list_capsules() first to pick a capsule_id and see its input/ output port names. Create any geometry with execute_rhino_script, then pass the returned GUIDs here. Inputs are validated client-side against the capsule manifest before anything is sent to Rhino, so errors from this tool name the exact port at fault.

Input value forms (keyed by the exact ALMOND_IN_* port name):

  • Geometry ports (point/curve/mesh/brep and their [] list forms): {"guids": ["", ...]} referencing objects already in the Rhino document.

  • number / integer / string / bool ports: the raw JSON value.

  • number[] / integer[] / string[] ports: a flat JSON list. Units are declared per port in the manifest — supply values in those units; nothing is guessed or auto-converted client-side. Optional inputs may be omitted; the capsule's declared defaults then apply inside the definition.

Only audited capsules run. audited=false means the GHX has no verified ALMOND_IN_*/ALMOND_OUT_* harness params yet; this tool refuses it (see capsules/AUTHORING.md for how to harness one).

The response's "outputs" object is keyed by ALMOND_OUT_* names; interpret each value using the manifest's semantics field (e.g. max_nodal_displacement, per_element_utilization) and declared units. The response also carries analysis_method (api|template|rule_based), confidence (high|medium|low), warnings, and baked_guids — report method and confidence honestly; rule_based/low means an estimate, not FEA.

Args: capsule_id: Stable capsule identifier from list_capsules (e.g. "karamba_beam_v1"). inputs: Object mapping ALMOND_IN_* port names to values (forms above). seed: Optional integer seed for stochastic definitions; omit for deterministic ones. timeout_s: Solver budget in seconds (default 60, max 600). Returns: JSON with status, capsule_id, outputs, baked_guids, analysis_method, confidence, warnings, and error — or a validation error naming the offending input and the valid port names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
inputsYes
timeout_sNo
capsule_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Without annotations, the description fully discloses behavior: client-side validation, exact port error naming, input value forms (geometry, atomic, arrays), unit handling, optional input defaults, and response structure including analysis_method and confidence. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, workflow, input forms, restrictions, response) but is somewhat verbose. Every sentence adds value, but it could be more concise without losing information.

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 complexity (4 params, nested objects, output schema), the description is highly complete, covering input formats, validation, output interpretation, and error cases. It complements the output schema with context on semantics and confidence levels.

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

Parameters5/5

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

Schema coverage is 0%, but the description explains all 4 parameters in detail: capsule_id from list_capsules, inputs with complex structure (geometry GUIDs vs simple values), optional seed for stochastic definitions, timeout_s with default and max. This adds significant meaning 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 runs an audited Grasshopper capsule through the Rhino bridge and returns outputs. It distinguishes from siblings by specifying the workflow involving list_capsules and execute_rhino_script, making the purpose specific and unique.

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?

The description provides explicit usage guidelines: call list_capsules first to get capsule_id and port names, create geometry with execute_rhino_script, then pass GUIDs here. It also states when not to use (non-audited capsules) and refers to authoring docs for alternatives.

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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