Get one AI tool
get_toolFull review detail for a single tool by its WireTensors slug (from search_tools results).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The tool's WireTensors slug, e.g. "chatgpt" or "deepseek" |
get_toolFull review detail for a single tool by its WireTensors slug (from search_tools results).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The tool's WireTensors slug, e.g. "chatgpt" or "deepseek" |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description must carry the full burden of behavioral disclosure. It states it returns 'Full review detail' but does not specify the structure, whether it is read-only, or any side effects. For a GET-like tool, this is minimal; an agent would not know what fields to expect or if any permissions are needed.
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, focused sentence with no filler. It front-loads the core purpose ('Full review detail for a single tool') and then provides the input source, making it efficient and easy to parse.
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 one parameter and no output schema, the description covers the purpose and parameter source, but lacks detail on the return format or any behavioral specifics. It is adequate for basic invocation but leaves the agent guessing about the structure of the 'full review detail' response.
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 schema already documents the slug parameter with an example. The description adds context by noting the slug comes from search_tools results, which clarifies the expected source. This adds value beyond the schema's type and description, so it earns a slightly above-baseline score.
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 returns 'Full review detail for a single tool' by its slug, which is a specific verb and resource. It distinguishes itself from search_tools by referencing that the slug comes from search_tools results, implying this tool is for retrieving details of one item rather than searching. However, it does not explicitly name a sibling alternative, so it lacks the full differentiation of a top-tier description.
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 a workflow: first use search_tools to obtain a slug, then use this tool for full details. This gives some usage context but does not explicitly state when to prefer this tool over siblings like compare_tools or recommend_tool, nor does it mention when not to use it. The guidance is present but implicit, not explicit.
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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