Make a product agent-native: from its OpenAPI, generate an MCP server (one tool per operation), a CLI, an llms.txt, and Claude Code/Codex/Cursor install snippets.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | ||
| openapiUrl | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what is generated, but not whether this triggers network calls, writes files, requires authentication, or how outputs are returned. The absence of side-effect or prerequisite information leaves an agent uncertain about what invoking this tool will do.
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, compact sentence that lists the key artifacts and the source format. It is front-loaded with the central purpose and avoids redundancy, though the opening phrase 'Make a product agent-native' is slightly jargon-heavy.
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's multi-output behavior, two parameters, zero schema coverage, no annotations, and no output schema, the description does not provide enough context. An agent cannot tell exactly what it must pass, what the generated artifacts look like, or what the tool actually returns after generation.
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 compensate for both 'domain' and 'openapiUrl'. It only hints that an OpenAPI source is relevant, without explaining what 'domain' means, where 'openapiUrl' is resolved from, or which parameter takes precedence when both are supplied. This is insufficient for a tool with two undocumented parameters.
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 a clear action ('generate') and a distinct resource: product agent interfaces built from an OpenAPI document. It enumerates the concrete outputs (MCP server, CLI, llms.txt, install snippets), which clearly separates this generator tool from the research/registry analysis tools in the sibling list.
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 it should be used when a product's OpenAPI should be turned into agent-facing artifacts, but it never states explicit conditions for use or warns when not to use it. No alternative tools are referenced, so an agent must infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
resolve and find_tools_for_task both return ranked tool recommendations for a task, and how_to also surfaces recommendedTools, making their boundaries unclear. search_tools adds further overlap as a registry search by query and requirements. The company/research and registry lookup tools are more distinct, but the task-to-tool cluster is genuinely confusing.
The naming is almost entirely snake_case verb_noun: get_company, find_competitors, search_tools, compare_products, list_registry. The exceptions are audit and resolve as bare verbs and how_to as an idiom, but they are still recognizable.
Fifteen tools is at the upper end of a reasonable range, and the broad scope of registry lookup, research, comparison, audit, and interface generation supports a larger surface. However, find_tools_for_task largely duplicates resolve, so the count is slightly higher than necessary.
The server covers the main registry lifecycle: listing, searching, getting records, comparing, researching, pricing, readiness auditing, and generating agent interfaces. It is intentionally read/research-oriented, so the lack of registry CRUD is not a severe gap. Minor missing pieces like direct per-product OpenAPI retrieval or registry entry management are workable around.