EcoCompute Should-I-Quantize Estimator

search_measurements

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.

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": {
      "query": "<query>"
    },
    "name": "search_measurements"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	model ('TinyLlama'), GPU ('4090'), architecture ('ampere') or size ('7')

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 24, 2026

TDQS

A3.6/5.0

Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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.

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