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calculate_effective_capacitance

Read-onlyIdempotent

Effective capacitance of a Class II MLCC at real operating conditions — the marked value is not the value in circuit. Multiplies the four loss mechanisms that compound: DC bias (the dominant one, typically 35-65% loss at rated voltage and…

Instructions

Effective capacitance of a Class II MLCC at real operating conditions — the marked value is not the value in circuit. Multiplies the four loss mechanisms that compound: DC bias (the dominant one, typically 35-65% loss at rated voltage and worse in small cases), AC signal level, temperature coefficient, and logarithmic dielectric ageing, then applies the part tolerance. Returns the full factor stack so you can see which mechanism hurt most. Class I (C0G/NP0) parts correctly short-circuit to tolerance-only. Pass measured_retention_at_bias from a datasheet curve (Murata SimSurfing, TDK SEAT) to replace the band estimate and get confidence='measured'.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "marked_capacitance_uf": "<marked_capacitance_uf>",
      "rated_v": "<rated_v>",
      "applied_v": "<applied_v>"
    },
    "name": "calculate_effective_capacitance"
  }
}

Input Schema

NameRequiredDescriptionDefault
rated_vYesThe part's rated DC voltage.
ac_levelNoSignal magnitude class: small_signal (<50 mVrms), moderate (50-500 mVrms), or large (>500 mVrms). Small signals see less capacitance.small_signal
ambient_cNoOperating ambient temperature in °C.
applied_vYesDC bias actually across the part in this circuit, in volts.
case_sizeNoEIA case code: 0402, 0603, 0805, 1206 or 1210. Larger cases retain more capacitance under bias.0805
dielectricNoEIA Class II dielectric: X5R, X7R, X8R, Y5V, Z5U, or Class I C0G/NP0.X5R
tolerance_pctNoThe part's capacitance tolerance in percent (e.g. 10 for ±10%). Applied at the pessimistic end.
hours_in_serviceNoExpected service life in hours, used for the log-time ageing term.
marked_capacitance_ufYesThe capacitance printed on the reel, in µF.
measured_retention_at_biasNoRetention fraction (0-1] read from the real datasheet DC-bias curve at applied_v. Replaces the band estimate.

Output Schema

NameRequiredDescriptionDefault
notesYesHuman-readable findings: which term dominates, what to change, and any caveat.
sourceYesThe measurements and standards the model is calibrated against.
rated_vYesThe part's rated DC voltage.
loss_pctYesPercentage of the marked value that is lost.
ambient_cYesOperating ambient used.
applied_vYesDC bias across the part that was analysed.
case_sizeYesEIA case code analysed.
confidenceYesHow trustworthy the number is: 'measured' from a datasheet curve, 'band' from a published retention range, 'physical' for Class I parts where the loss is zero by physics.
dielectricYesEIA dielectric class analysed.
ageing_factorYesRetention after the service life, log-time law.
dc_bias_factorYesRetention from DC bias alone — usually the dominant term.
ac_level_factorYesRetention from AC signal magnitude (ferroelectric hysteresis).
hours_in_serviceYesService life used for the ageing term.
tolerance_factorYesRetention at the pessimistic end of the part tolerance.
retention_fractionYeseffective / marked, after every loss mechanism.
temperature_factorYesRetention at the operating temperature, worst-case end of the TCC band.
marked_capacitance_ufYesThe value printed on the reel.
effective_capacitance_ufYesThe capacitance the circuit actually gets.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.16.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), and the description adds substantial model behavior: four multiplicative loss mechanisms, which one dominates (DC bias, 35-65% at rated voltage, worse in small cases), the fact that tolerance is applied at the pessimistic end, and that the response exposes a per-factor breakdown plus a confidence label. This is exactly the kind of domain-specific behavior an agent needs and cannot get from schema or annotations.

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?

Front-loaded with the core insight (marked ≠ in-circuit) and structured from model → return shape → optional measured path. Slightly padded by rhetorical framing ('the marked value is not the value in circuit', 'see which mechanism hurt most'), but no sentence is truly wasted.

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 10-parameter, 3-required engineering calculator with an output schema, the description covers the model, the dominant failure mode, the Class I edge case, and the measured-input escalation path. Nothing an agent needs in order to call it correctly or interpret the result is missing.

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

Parameters4/5

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

Schema description coverage is 100%, so baseline would be 3, but the description adds meaning the schema does not: it explains that measured_retention_at_bias supplants the internal band estimate and changes the returned confidence field, and that Class I dielectrics bypass the loss stack entirely. That linkage between an input and an output attribute is genuine added semantics.

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?

States a specific verb (compute effective capacitance) and a precisely bounded resource (Class II MLCC at real DC-bias/temperature/age conditions), and immediately distinguishes itself from the naive marked-value calculation. An agent can tell this apart from siblings like size_hold_up_capacitor or analyze_rc_filter_effective without opening any schema.

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?

Gives a clear trigger (real in-circuit derating, not the reel value) and an explicit mode switch: supply measured_retention_at_bias from a Murata SimSurfing / TDK SEAT curve to get confidence='measured' instead of the band estimate. It also names the case where it degenerates (C0G/NP0 → tolerance-only). It stops short of naming sibling tools to use instead, so not a 5.

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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