Hologram Model Hub

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

No annotations are present, so the description carries the burden of disclosure. It does provide meaningful behavioral detail by listing the output facets, including 'pinned revision' and 'current health' of sources, which implies live or status-dependent data. However, it does not disclose error behavior, auth requirements, or potential performance considerations such as large file lists.

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 a single dense sentence that front-loads the primary result (pinned revision) and then lists the remaining returned data with no wasted words. Every phrase contributes meaning and there is no repetition of the tool name or title.

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 getter with no output schema, the description is quite complete: it tells the agent what data will come back and gives enough context to interpret the result. It omits error handling or pagination details, and could mention what happens when a model is not found, but the core invocation needs are satisfied.

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 sole parameter id is fully documented by the input schema ('org/name, as on Hugging Face'), reaching 100% schema coverage. The description adds no additional meaning about the parameter's format, constraints, or edge cases, so the baseline of 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 clearly identifies the resource ('one model') and enumerates the exact returned data: pinned revision, files with size and SHA-256, GGUF quantisations, and source health. The title 'Get a model' supplies the verb. It is distinguishable from siblings like resolve_file and search_models, though it does not explicitly contrast them.

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?

There is no guidance on when to use this tool versus search_models or resolve_file, and no exclusion criteria or prerequisites are mentioned. The context is only implicit: this tool retrieves details for a single model, while siblings search or resolve files. The description does not help an agent decide between alternatives.

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 transparency burden and does a good job: it states the tool returns download coordinates, verifies/pins a SHA-256, and produces engine-specific commands. It implies a read-only information lookup, though it does not explicitly discuss failure modes or side effects.

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 a single dense sentence that front-loads the main purpose and then lists what the caller needs to provide. It is efficient, though the list-like structure could be broken out for easier scanning.

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 three-parameter, read-only resolver with no output schema, the description covers the key return contents (download location, SHA-256, verification, engine commands) and the key input constraint (path or quant). It stops short of giving examples or explicit conflict rules between path and quant, but is largely sufficient.

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 0%, so the description must explain the parameters. It does explain path and quant by saying to supply either a path or a GGUF quantisation like Q4_K_M, but it leaves the required id parameter underspecified and does not state how id relates to the other fields.

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 identifies a concrete function: given a model file, return its download location, SHA-256, verification steps, and engine commands. This clearly differentiates it from sibling tools, which handle model metadata and search, even though it never uses the verb '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?

The phrase 'Where to download one file of a model right now' implies the use case, but the description never explicitly says when to choose this over get_model or search_models, nor does it mention exclusions or prerequisites. The context is understandable but not fully 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, the description must carry the full burden. It discloses that every result has files addressed by SHA-256 and lists the returned fields, but it does not mention pagination, permissions, or other side effects. Some behavioral context is present, but it is not comprehensive.

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 concise sentences with no filler. It front-loads the purpose and then adds relevant return details. It is appropriately sized for a tool with 7 optional parameters.

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 tool has 7 optional parameters and no output schema, the description explains the return fields and the SHA-256 addressing, which is helpful. However, it omits pagination behavior and does not clarify the result structure (e.g., array vs single object), leaving minor gaps for an agent invoking it correctly.

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 71%, so most parameters already have descriptions. The description itself adds no parameter-specific meaning—it only lists return fields. The uncovered parameters (sort, limit) are self-explanatory from their enums/defaults, so the schema does the heavy lifting and the description does not compensate further.

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 'Find open models in the hub's index' with a specific verb and resource, and adds the SHA-256 detail. It is unambiguous, but it does not explicitly contrast with sibling tools get_model or resolve_file, so it lacks explicit differentiation.

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 this is the tool for searching models, but it does not specify when to use it instead of get_model or resolve_file, nor does it provide exclusions or conditions. 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.