get_ai_tool
Get the full record for one AI tool by name (exact match first, then fuzzy).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Tool name, e.g. "ChatGPT". |
Get the full record for one AI tool by name (exact match first, then fuzzy).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Tool name, e.g. "ChatGPT". |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It goes beyond the schema by revealing the matching strategy ('exact match first, then fuzzy'), which is valuable context. However, it does not disclose not-found behavior, error handling, or what 'full record' concretely contains.
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?
A single sentence delivers the core action, resource, and matching behavior with no filler. The parenthetical adds important nuance without bloating the definition.
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 simple one-parameter retrieval tool with no output schema or annotations, the description covers the primary action and match behavior. It is slightly lean on return expectations (what exactly 'full record' contains) and missing-not-found handling, but these are minor for a get-by-name tool.
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 the baseline is 3. The description adds meaningful semantics beyond the schema by explaining how the name parameter is interpreted during matching ('exact match first, then fuzzy'), which helps the agent know that partial or imprecise names may still resolve.
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 uses a specific verb ('Get') with a clear resource ('full record for one AI tool') and specifies the lookup mode ('by name'). It distinguishes itself from sibling search_ai_tools by emphasizing retrieval of a single full record by name rather than searching.
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 clearly implies this tool is for retrieving a single known tool by name, which separates it from search_ai_tools and find_free_ai_tools. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full usage guidance.
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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