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

There are no annotations, so the description carries the full behavioral burden. It partially does: it distinguishes REAL (measured on a named machine/date) from MODELAT (computed as a range), which is useful provenance and uncertainty disclosure. But it doesn't say whether this is a read-only lookup, whether it caches, whether it returns a list or a single answer, or what the output looks like (no output schema). The REAL/MODELAT distinction is a strong addition, but important behavioral gaps remain for a tool with zero annotation coverage.

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 three tight sentences with no obvious waste, front-loading the core action and then adding the MoE differentiator and data provenance. It is well-structured and readable. It could be even leaner, but it 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?

For a calculation/estimation tool with no annotations and no output schema, the description should ideally state what the return looks like (list, table, single recommendation) and any constraints. It covers the input side and the REAL/MODELAT labeling, which is valuable, but omits output shape and any failure conditions. It is adequate but incomplete for a tool with no structured behavioral aids.

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%, and the schema already documents all four parameters (cuant, placa, ram_gb, tip_ram) with examples and defaults. The description adds no parameter-specific syntax or format details beyond what the schema provides. It does restate the core inputs (graphics card and RAM) at a high level, but does not compensate for anything the schema lacks. 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 states a concrete verb+resource: given a GPU and RAM, it answers which language models will run and how fast. This is specific and not a tautology of the title. It is clear what the tool does, though it doesn't explicitly distinguish itself from the sibling tools (cauta_dupa_nevoie, cauta_pe_raft, compara, etc.), whose names suggest search/comparison utilities. The differentiation is implicit, not stated.

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 explains that it differs from 'ordinary VRAM calculators' by supporting mixture-of-experts models with experts in system RAM, which implies when to use it (you want fit/performance estimates, not raw VRAM math). However, there is no explicit statement of when not to use it, no alternative named, and no prerequisites (e.g., 'requires a known GPU id'). Usage is implied, not spelled out.

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