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IC-CAP MCP Server

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transform_set_optim_field

Set optimizer transform table fields (Inputs, Parameters, Options) with algorithm-aware validation, preventing invalid configurations in IC-CAP.

Instructions

Set an Optimize Transform's table field value via STFV.

Configure IC-CAP optimizer Inputs, Parameters, and Options tables programmatically. Option field names are validated against the current algorithm; invalid names return success=false with an explanation.

Common fields:

  • Function: Set to "Optimize" to enable the optimizer

  • Algorithm: Optimization algorithm (full names or PEL short codes, auto-mapped). Unknown values are rejected before reaching IC-CAP (the native SetAlgorithm accepts anything without checking). On success the response carries current_value with current_value_source='this write': IC-CAP provides no way to read the algorithm back, so your own write is the authoritative record.

    • Full names: "Levenberg-Marquardt", "Random", "Hybrid", "Gradient", "Quasi-Newton", "Minimax", "Genetic", "Least Pth"

    • Short codes: "L", "R", "H", "B", "G", "N", "Q", "I"

  • Error: Error type (full names or PEL short codes, auto-mapped). Same write-only caveat as Algorithm.

    • Full names: "Relative", "Absolute" Short codes: "R", "A"

  • Inputs table (0-indexed rows):

    • "Target[i]": Target measured data, e.g. "ia.m"

    • "Simulated[i]": Simulated data, e.g. "ia.s"

    • "X Min[i]", "X Max[i]": X-axis range limits

  • Parameters table (0-indexed rows):

    • "Name[i]": Parameter name, e.g. "MAIN.IS"

    • "Min[i]": Parameter minimum bound

    • "Max[i]": Parameter maximum bound

  • Options table (algorithm-specific!):

    • Random (R): RMS error, Rand Iters, Rand Std Dev, Rand Reward, Rand Penalty, Rand Seed, Comb Filter

    • LM (L): RMS error, Max error, Max Evals, Param Tol, Function Tol, Comb Filter, Param Delta

    • Hybrid (H): RMS error, Max error, Rand Iters, Comb Filter, Param Tol, Function Tol, Param Delta

    • Minimax (N): RMS error, Max error, Max Evals, Param Tol, Function Tol, Max Iters, Comb Filter

    • All algorithms also support: X/Y Lower/Upper Bound

    • NOTE: Random uses 'Rand Iters' (NOT 'Max Iters'), LM uses 'Max Evals' (NOT 'Max Iters')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesValue to set
field_nameYesField name with optional row index, e.g. Target[0], Name[1], Min[BF]
transform_pathYesOptimize Transform path

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral disclosure burden and does so thoroughly. It reveals validation behavior ('invalid names return success=false with an explanation'), pre-validation before IC-CAP ('unknown values are rejected before reaching IC-CAP'), and the write-only caveat for Algorithm and Error fields, including the fact that IC-CAP provides no read-back mechanism.

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 long, but its length is earned given the algorithmic complexity. It front-loads the one-sentence purpose, then organizes details into Common fields, Inputs/Parameters/Options sections, and a final note clarifying the Random/LM/Max Iters distinction. Each section adds necessary information without significant redundancy.

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?

For a tool with no output schema, the description covers all needed context: accepted field names, valid values, validation failure semantics, algorithm-specific behavior, and the authoritative-write caveat. An agent can confidently construct valid calls for any of the listed algorithms and understand potential failure responses.

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 100% but the schema descriptions are minimal ('Value to set', 'Field name with optional row index'). The description massively enriches parameter semantics by documenting valid Algorithm/Error full names and short codes, the 0-indexed row syntax, algorithm-specific Options fields, and examples for Target[i], Simulated[i], Name[i], Min[i], etc.

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 opening sentence 'Set an Optimize Transform's table field value via STFV' uses a specific verb and resource, and the second sentence clarifies it configures IC-CAP optimizer Inputs, Parameters, and Options tables. This clearly distinguishes it from sibling read tools like transform_get_optim_field and from generic setters like input_set_field.

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 gives clear context about when to use this tool: when programmatically configuring optimizer table fields. It lists common fields and their meanings, implicitly scoping usage to Optimize Transform tables. It does not explicitly state exclusions or name alternative tools for the same job, but the specialization is strong enough that an agent can route correctly.

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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