Energy impact of LLM quantization, calibrated to measured GPU data. Every answer states its basis.
Scored across 4 tools
The four tools have mostly distinct purposes: estimating energy impact, retrieving a crossover threshold, reporting data coverage, and searching measured anchors. There is some potential confusion between estimate_quantization_energy and get_quantization_crossover when answering a yes/no question about whether quantization saves energy, but the descriptions make the distinction clear.
All tool names follow a consistent snake_case verb_noun pattern: estimate_*, get_*, list_*, search_*. The naming is predictable and readable across the set.
Four tools is well-scoped for a focused estimator service. Each tool earns its place by covering a distinct workflow: estimation, threshold lookup, coverage inspection, and raw evidence search.
The surface covers the core lifecycle for this read-only estimation domain: produce an estimate, find the crossover threshold, inspect data coverage, and search the underlying measured evidence. No obvious operation is missing for the stated purpose.
Estimate the task-level energy change (ΔE% vs FP16) of weight-only quantization (NF4 or INT8) for a model of a given size on a GPU architecture class, from measured-anchored curves. The answer carries basis (measured/interpolated/estimated), confidence and evidence boundaries.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | alternative: plain-English query, e.g. '13B on A100 int8 batch 32' | |
| ctx | No | context length (default 2048) | |
| arch | No | GPU architecture class | |
| batch | No | batch size (default 1; >1 shrinks savings — modelled, not measured) | |
| params_b | No | model size in billions of parameters, e.g. 7 | |
| precision | No | quantization format (default NF4) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It usefully discloses that results come from measured-anchored curves and that the answer carries basis (measured/interpolated/estimated), confidence and evidence boundaries, which is meaningful behavioral context. However, it does not clarify read-only safety, whether any parameters are required (schema says none), precedence between the free-text 'q' and structured fields, or defaults - notable gaps for a tool with zero required params and no annotations.
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?
Two dense sentences with the core purpose front-loaded and no filler. The append about basis/confidence/evidence boundaries earns its place, though the phrasing is slightly jargon-heavy.
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 tool with no output schema and six optional parameters, the description partially covers the return (basis, confidence, evidence boundaries) but does not spell out the primary output field or how ambiguity among optional inputs is resolved. This is adequate but leaves meaningful gaps an agent would need to discover by trial.
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 parameter meanings are already fully documented, including defaults for ctx, batch and precision and that batch>1 savings are modelled rather than measured. The description adds only the framing of model size/GPU architecture/precision as the estimation inputs, which is close to baseline for a fully-covered 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?
States a specific verb and resource ('Estimate the task-level energy change (ΔE% vs FP16) of weight-only quantization') with a precise output quantity and input scope. It is clear what it does, but it never mentions the sibling get_quantization_crossover or explains why one would pick this estimator over that tool.
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 by the tool's purpose (estimating quantization energy savings), but there is no explicit when-to-use/when-not-to-use guidance and no reference to the crossover or measurement-search siblings that an agent would plausibly confuse it with. It also does not say whether a plain-English query is preferred over structured parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the model-size crossover for NF4/INT8 per architecture: below it quantization costs energy, above it saves. Crossovers belong to a card + software stack, not an architecture — the answer says so.
| Name | Required | Description | Default |
|---|---|---|---|
| arch | No | optional: limit to one architecture |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden. It usefully discloses that crossovers belong to a card and software stack rather than an architecture and that the answer reflects this, but it does not describe return format, units, or other operational traits.
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?
Two sentences, front-loaded with the core purpose, followed by an important interpretive caveat. There is no redundant or wasted text.
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?
The description gives a useful conceptual picture of the return value: a crossover size and what it means above/below. But with no output schema and no annotations, it omits concrete details such as units, how NF4 versus INT8 results are presented, and how multiple architectures are returned when arch is omitted.
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 optional architecture enum is already fully documented in the schema. The description reinforces the 'per architecture' framing and adds a caveat about interpretation, but does not add parameter syntax or format details 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 uses a specific verb and resource: 'Get the model-size crossover for NF4/INT8 per architecture,' and explains what the crossover means. It is clearly distinct from a generic energy-estimation tool, though it does not explicitly name sibling tools.
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 the crossover is useful by explaining that below it quantization costs energy and above it saves. However, it does not explicitly say when to use this tool versus estimate_quantization_energy or other alternatives, nor does it state any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List what is actually measured (GPU architecture × precision × size range, NVML direct), what is borrowed/estimated, and where the gaps are. Use this before trusting an estimate at the edges of coverage, and to answer 'is there data for my GPU?' honestly.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and no output schema, the description carries the full disclosure burden. It does usefully expose data provenance (NVML direct vs borrowed/estimated) and that gaps are surfaced, but says nothing about the response structure, granularity, or size, leaving the agent guessing at what the list actually looks like.
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?
Two tight sentences, front-loaded with the content and followed by usage triggers. The trailing 'honestly' is mild filler but doesn't undermine the structure.
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 zero-parameter, annotation-free tool with no output schema, the description does the essential job of explaining what the return conceptually contains (measured vs estimated vs gaps). A sentence on the output shape would close the remaining gap.
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?
The tool takes zero parameters, so there is nothing to disambiguate; baseline 4 applies. The description correctly implies no input is required.
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 ("List") and a precisely scoped resource: coverage across GPU architecture × precision × size range, broken into measured, borrowed/estimated, and gaps. This is clearly separable from the sibling tools that produce estimates or search raw measurements.
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?
Gives concrete when-to-use triggers: before trusting an estimate at the edges of coverage, and to answer 'is there data for my GPU?'. It implies the contrast with estimate_quantization_energy but never names alternatives or states explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the measured anchors by model name, GPU name, architecture or size. Returns ΔE% vs FP16 per anchor with run counts — the raw evidence behind the curves.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | model ('TinyLlama'), GPU ('4090'), architecture ('ampere') or size ('7') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose the return payload ('ΔE% vs FP16 per anchor with run counts'), which is valuable since there is no output schema, but it says nothing about result limits, pagination, or whether an empty query result is possible — gaps that matter for a search tool with no structured safety hints.
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?
Two sentences, no filler. The retrieval action and searchable facets come first, and the return-value note follows — properly front-loaded.
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 one-parameter read-only search tool with no output schema, the description covers both what goes in and what comes back (metric plus run counts). Only the operational details — result set size, ordering, empty-result behavior — are absent, which keeps it short of full completeness.
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% and the single 'query' parameter already documents the same four interpretation modes (model, GPU, architecture, size) with examples. The description restates those facets without adding matching semantics such as partial-match or case behavior, 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?
States a specific verb and resource ('Search the measured anchors') plus the facets it operates on, and the phrase 'the raw evidence behind the curves' positions it against the sibling tools that return derived curves/estimates. It stops short of naming a sibling explicitly, so it is clear but not maximally differentiated.
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 by 'raw evidence behind the curves' — an agent can infer this is the drill-down tool when it needs underlying data points rather than derived quantities. No explicit when-to-use, when-not-to-use, or reference to estimate_quantization_energy / list_data_coverage is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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