list_technologies
Technologies, optionally by kind (language, discipline, platform, framework, protocol, runtime, datastore, hardware, model).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No |
Technologies, optionally by kind (language, discipline, platform, framework, protocol, runtime, datastore, hardware, model).
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing: no read-only confirmation, no pagination/limit behavior, no ordering, and no indication of what happens with an unrecognized kind value. Only the implicit 'list' semantics give any signal.
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?
One compact sentence with no filler, and the essential enum list is placed where it can be scanned immediately. It is arguably under-specified rather than padded, so the terseness costs clarity slightly but wastes nothing.
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 list tool with no output schema and no annotations, the description covers the parameter domain but leaves the return shape and result size unaddressed. An agent knows the valid filter values but not what a call yields.
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 0% and the single 'kind' parameter has no description in the schema, so the description must compensate — and it does, enumerating nine valid values (language, discipline, platform, framework, protocol, runtime, datastore, hardware, model) that an agent could not otherwise know. It stops short of saying whether values are case-sensitive or whether other values are accepted.
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 names the resource ('Technologies') but supplies no verb; the read/list action is only inferable from the tool name list_technologies. The parenthetical enumeration of kinds is informative, yet the sentence never says what the tool actually does with them (enumerate matching entries).
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?
Only the phrase 'optionally by kind' hints at usage — it implies the filter is optional and unfiltered calls return everything. There is no statement of when to prefer this over sibling tools such as list_systems or list_capabilities, and no exclusions or prerequisites.
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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