MakerPortal compute

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

With no annotations, the description carries the full behavioral burden and does so richly: it enumerates the returned blocks (per-rate coefficients, pole/zero geometry, bisection-solved −3 dB points, Q sweep, gain sweep, quantised forms), discloses that the curve is omitted unless requested, and explains the non-finite encoding convention and its rationale. The provenance.canonicalUrl guarantee is also documented.

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?

Front-loaded with the one-line purpose before the output inventory, and each subsequent sentence conveys distinct information (returned blocks, omitted curve, non-finite encoding, provenance). It is dense but not padded, though the non-finite encoding aside is slightly discursive for a tool description.

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?

An output schema exists, so return values need not be explained, yet the description still gives enough to anticipate the payload structure and the optional curve block. With no annotations and a 6-parameter schema, nothing an agent needs to invoke this correctly is missing.

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 description coverage is 100%, so the schema already documents all six parameters including the enum for include and the shelf/gain caveats. The description mostly restates the include=curve behavior already in the schema, adding no format or constraint detail beyond it — the correct baseline when the schema does the heavy lifting.

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?

States a specific verb (design) and resource (one RBJ-cookbook biquad) plus the reporting scope, so an agent immediately knows this is a DSP filter-design calculator. Siblings (gaussianBeam, llmVramFit, roomModes, etc.) are entirely unrelated domains, so there is no ambiguity to resolve.

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?

It anchors the tool to a concrete referent ('the identical function behind the /lab/biquad coefficient tables') and states the condition for the heavy include=curve block, which is implied usage guidance. However it never says when to prefer this over other approaches or when not to call it; usage is inferred rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the behavioral burden and does so well by explaining the q-propagation model, conditional result fields, and the provenance.canonicalUrl included in every result. It could add more about failure modes or input constraints, but the core calculation behavior is disclosed.

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 front-loaded with the core purpose and then proceeds through optional inputs and provenance in a logical order. The opening sentence repeats the title, but the rest is efficient and 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?

Given the output schema exists, the description does not need to detail return fields, and it instead supplies the missing behavioral and conditional-input context. It is complete enough for an agent to invoke the tool correctly with or without the optional parameters.

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 description coverage is already 100%, so the baseline is 3, but the description adds relational meaning by explaining that `distance` is from the waist unless `focal` places it from the lens, and that `focal` creates a focused-waist result. This goes beyond the individual parameter descriptions in 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 states a specific verb and resource: solving an ideal Gaussian laser beam, with named outputs including Rayleigh range, divergence, radius at a distance, and focused waist. It is clearly distinct from the listed sibling tools, which are unrelated calculators except for the broader opticsResonator.

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 clear context for use by explaining what the two required inputs return and how adding `distance` or `focal` changes the output. It does not name an alternative tool or explicitly state when not to use this one, but no close alternative is present among the siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden and does a good job: it enumerates the per-quantization output fields (weight bytes and basis, headroom, largest fitting context, decode ceiling), names the five-state verdict and best-quant selection, and discloses data provenance (each repo's config.json, tensor-shape index, the site's device table). It does not explicitly state that the call is read-only/non-mutating or whether results are deterministic or cached, so it falls short of a 5.

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?

Purpose is front-loaded in the first line, followed by output and provenance detail in a logical order, with no filler sentences. It is somewhat longer than strictly necessary and repeats the return detail that the output schema already supplies, keeping it out of the top band.

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?

An output schema exists, so return values need not be re-explained, yet the description supplies useful sourcing and provenance context (config.json, device table, canonicalUrl) that the schema does not cover. Combined with the fully documented input schema, an agent has everything needed to call it, though read-only semantics remain implicit.

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 description coverage is 100%, so all five parameters (including the batch/context/kvBytes defaults) are already fully documented in the schema, setting the baseline at 3. The description adds only adjacent context (the quantization range and provenance behavior) rather than new per-parameter meaning.

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 title/description state a specific decision task ('decide whether one LLM fits one accelerator') plus an explicit scope ('at every quantization'). It also identifies the underlying instrument ('The identical function behind the /lab/llm-vram instrument'), making the tool's identity unambiguous. Siblings are unrelated domains, so no differentiation burden remains.

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 makes the use case clear (evaluate a model/GPU pair across quantization levels) and notes the published-instrument parity, but states no when-to-use/when-not conditions or any alternative approach. With no sibling overlap there is little to exclude, but the guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the burden well: it discloses the exact stability return fields, the crucial edge case that `mode` is null for unstable cavities and for the g1*g2 = 0 or 1 boundary, and that every result includes provenance.canonicalUrl. It doesn't discuss side effects or failure modes, but as a pure computation these are largely moot.

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?

Three short paragraphs, front-loaded with the core purpose, then inputs, then return shape and provenance. Dense but every sentence carries information; the one red flag is that the first sentence duplicates the tool title verbatim.

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 deterministic 4-parameter compute tool with a full input schema and an output schema, the description covers inputs, units, conventions, return structure, the null/edge-case behavior, and provenance. Nothing an agent needs to invoke it correctly is missing.

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 description coverage is 100%, so the schema already documents units and the r1/r2 sign convention; the description restates them ('positive is concave toward the cavity, negative convex, omitted means flat') without adding syntax or new meaning. Baseline 3 is appropriate when the schema does the heavy lifting.

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 opens with a specific verb and resource ('Solve a two-mirror laser cavity') and enumerates the exact outputs (g-parameters, stability, TEM00 waist, mirror spot sizes). This clearly separates it from siblings like gaussianBeam, roomModes, and solenoidField.

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 implies when to use it by listing the required inputs and the context ('the resonator code the bench runs'), but it names no alternatives and gives no explicit when-not guidance (e.g., how it differs from gaussianBeam for propagating a beam). Usage is inferable rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden, and it does substantial work: it lists the returned quantities and, critically, discloses the edge case that 'When the port is too narrow, tooNarrow is true and cutLengthCm is negative'. It also discloses that every result carries provenance.canonicalUrl. It does not explicitly state the tool is a side-effect-free calculation, which keeps it short of a 5.

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 purpose is front-loaded and the body is dense with useful detail. The opening sentence duplicates the title verbatim, which is mild waste, but the remaining sentences each add output, edge-case, or provenance information rather than padding.

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?

Although an output schema exists (so return values need not be explained), the description still documents the notable outputs and the counter-intuitive tooNarrow/negative-length condition plus provenance, which an agent benefits from before calling. With 6 parameters and no annotations, this is close to complete; only an explicit statement that the tool is a pure computation is missing.

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, but the description adds meaning the schema lacks — most notably that 'temp (deg C, default 20) sets the speed of sound', i.e. why the parameter matters, rather than just restating its units. It also frames sd/xmax as a required pair for air speed, complementing the schema's individual notes.

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 states a specific verb and resource — 'Solve a vented loudspeaker box for the length of round port to cut' — and enumerates the outputs (end corrections, smallest workable diameter, peak port air speed). Against siblings like biquadDesign, opticsResonator, and solenoidField, it is unmistakably the acoustic-port calculator, so an agent can discriminate without opening a schema.

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?

It names the three required inputs and gives explicit conditional guidance: 'Add sd (cone area, cm^2) and xmax (peak excursion, mm) together for the peak port air speed', clarifying that the optional pair is jointly needed. There is no when-not guidance or alternative tool named, but the siblings occupy unrelated domains so no exclusions are warranted.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden and does well: it discloses the ft→m conversion at 0.3048, that the full mode list is omitted unless include=modes is passed, that counts are always present, the 200 Hz cap rationale for pile-ups, and a concrete failure mode (400 once enumeration exceeds 100,000 modes, with the error naming a workable maxFreq). It stops short of stating cost/latency for large solves beyond the refusal threshold.

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?

Purpose is front-loaded in the first sentence, but the back half enumerates return contents (axial fundamentals, pile-ups, widest axial gap, Schroeder frequencies, provenance) in detail despite an output schema existing that should already cover this. Several clauses are packed densely and could be trimmed without losing call-relevant 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?

For a five-parameter compute tool with a full-coverage schema and an output schema, the description supplies the extra behavior an agent needs: units conversion, the include gate, error behavior, and provenance. Since the output schema already documents returns, the return enumeration is redundant rather than a gap; the only real omission is any statement of compute cost for large solves below the refusal threshold.

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 baseline is 3, but the description adds real meaning: the 0.3048 m/ft conversion for the dimension params, the reason maxFreq is bounded (mode count scales with volume × frequency³), and the concrete consequence of exceeding it. It largely reinforces rather than extends the include enum.

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?

Specific verb+resource: 'Solve the eigenmodes of a rectangular room,' plus the identity link to the /lab/room-modes instrument, which pins down scope precisely. It never names or distinguishes itself from the close sibling roomShapeBand, so an agent choosing between the two gets no explicit disambiguation.

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 describes what the tool returns and how include=modes gates the heavy block, but gives no when-to-use guidance, no prerequisites, and no routing against siblings like roomShapeBand or portLength. The reference to the matching web instrument is contextual flavor, not usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries full responsibility and delivers richly. It discloses the return contents, the volume band behavior, refusal of out-of-range input, and the presence of provenance.canonicalUrl on every result. It even clarifies that out-of-range volumes are refused rather than served from a neighboring band.

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 front-loaded with the core purpose and then details outputs, constraints, and provenance in a logical order. It is somewhat verbose, and the sentence about the identical rendering function is slightly tangential, but nearly every sentence earns its place.

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?

This is a single-parameter tool with an output schema, and the description covers input units, valid range, refusal behavior, output fields, band guarantees, and provenance. Nothing essential for an agent to decide whether to call it and what to expect is missing.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema: it specifies the valid volume range, the band structure, and the refusal behavior for out-of-range values, which helps the agent reason about valid inputs.

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 a specific verb and resource: solving rectangular room proportions for a given volume. It is unambiguous about what the tool computes, but it does not explicitly distinguish itself from sibling tools such as roomModes, so it misses the top score.

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 usage context is implied: provide a room volume in cubic feet and receive proportions. It also gives a firm exclusion boundary by refusing volumes outside 800–5,000 cubic feet, but it never names alternatives or states when to prefer another tool, so the guidance is only implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden and does well: it declares units (millimetres), the exact linearity law ('the field is exactly linear in turns x current, so only that product changes the answer'), and the provenance behavior including the off-grid lane-hub fallback. It does not state that the call is a pure/deterministic computation with no side effects, but nothing misleading is present.

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 dense but front-loaded with the purpose, then the returned quantities, then the one genuinely non-obvious behavioral fact (provenance.canonicalUrl). The long enumeration of return quantities partially duplicates the existing output schema, which is the only real slack.

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 four-parameter physics solver with two required inputs, all ranges, and an output schema, the description supplies the missing context an agent needs: units, the turns×current invariance, geometry applicability, and the validity caveat for the closed form. Because an output schema already exists, its detailed return enumeration is redundant rather than necessary, keeping this from a 5.

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 description coverage is 100%, so the schema already documents turns, current, length and diameter including defaults and the turns×current linearity. The description reinforces the millimetre units and the linearity rule but adds no new parameter-level syntax or format beyond what the schema provides, so baseline 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 opens with a precise verb+resource: 'Solve the on-axis magnetic field of an air-core solenoid,' and anchors it to a concrete artifact ('the identical function behind the /lab/solenoid table'). The sibling tools (biquadDesign, gaussianBeam, roomModes, etc.) occupy entirely different domains, so there is no ambiguity to disambiguate.

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?

Usage is implied rather than stated: the agent can infer you call this when you need an on-axis solenoid field, and the 'closed form should not be used below this turn count' caveat is embedded in the returned data rather than the guidance. There is no explicit when-to-use/when-not statement and no named alternative or prerequisite.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.