FitLLM

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable details: returns verdict format, memory breakdown, max context, and concrete fix. No contradictions. However, it doesn't disclose potential errors or limits, keeping it from 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?

Two compact sentences pack purpose, usage guidance, and behavioral highlights without any wasted words. 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?

With no output schema, the description carries full burden for return values and covers them comprehensively. It mentions architecture awareness and concrete fix, but could mention error handling or missing models.

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. The description adds meaning beyond schema by explaining fuzzy matching, multi-GPU syntax, quantization defaults per platform, and mac_ram_gb as unified memory. This is helpful but not exhaustive.

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 it checks whether a specific LLM fits on a specific GPU or Mac, with a specific verb and resource. However, it does not explicitly distinguish from sibling tools like 'list_supported' and 'what_fits_on_hardware', missing the chance to prevent misuse.

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?

Provides explicit usage scenarios with example queries ('can I run <model> on my <GPU/Mac>?'), but lacks when-not-to-use guidance or alternatives, which prevents a 5.

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?

Annotations provide readOnlyHint and idempotentHint. The description adds purpose and notes that any HuggingFace model is also valid, which gives useful behavioral context beyond annotations. No contradictions detected.

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: first states purpose and resource, second adds important context about HuggingFace support. No wasted words, front-loaded with the primary action.

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?

No output schema exists; the description says it 'list[s] the built-in model names and hardware names', which implies a list of strings but lacks precise format details. Given tool simplicity, this is adequate but could specify structure.

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?

There are zero parameters, so schema coverage is 100% trivially. The description adds meaning by explaining the purpose of the list, which is sufficient for a parameterless tool.

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 the tool lists built-in model and hardware names for mapping user wording to exact names. It distinguishes itself from sibling tools (check_llm_fit, what_fits_on_hardware) by focusing on listing known names.

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 use when needing to know available built-in names for mapping, but does not explicitly say when not to use or mention alternatives. Sibling tool names provide context but the description lacks direct 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?

Annotations already indicate read-only and idempotent behavior. The description adds useful details: ranking order (models that fit first, biggest first), max context per model, and quantization assumption. It does not detail the exact output format, but for a ranking tool this is acceptable.

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 two focused sentences: first explains purpose with key details, second gives usage examples. No wasted words, highly efficient for agent consumption.

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 no output schema, the description explains what is returned (models that fit, in order, with max context). This is largely sufficient for an agent to understand behavior. Minor gap: no explicit mention of output format (e.g., list of model names), but implied.

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 baseline is 3. The description reinforces that 'gpu' or 'mac_ram_gb' must be provided but adds little beyond the schema's own descriptions. The tool's logic of using one or the other is already clear in 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 clearly states the tool ranks popular local LLMs that fit on given GPU or Apple Silicon Mac, specifying quantization and context length. It distinguishes from siblings by focusing on ranking fitting models for hardware, unlike 'check_llm_fit' (checking specific model) and 'list_supported' (listing hardware).

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 explicitly provides example queries ('what can I run?', 'best local model?') to indicate when to use. However, it does not directly compare with sibling tools or specify when not to use, slightly reducing clarity for alternative selection.

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