Search AI capabilities across AWS Marketplace and the Official MCP Registry.
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Average 3.9/5 across 2 of 2 tools scored.
The two tools address completely different needs: one provides market status (population and freshness), the other searches for AI capabilities. There is no overlap or ambiguity between them.
Both tool names use a two-word underscore convention (market_status, search_market), but the word order differs (noun_noun vs verb_noun). This minor inconsistency prevents a perfect score, though the pattern is still simple and readable.
With only two tools, the surface is thin for a marketplace server. While the scope is narrow, a single search and status tool feel minimal; more tools would typically be expected to cover browsing or details.
The server lacks any tool to retrieve detailed information about a specific AI capability after searching. This is a significant gap for a marketplace, as an agent cannot drill down into search results, limiting its utility.
market_statusBInspectReturn xOmniMarket source population and freshness status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden. 'Return' suggests a read-only status operation, but it does not specify behavior such as whether data is cached, whether it triggers refresh, or what 'freshness' means. Acceptable for a simple status tool, but minimal.
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 one concise sentence with no filler words. It front-loads the action and directly names the resource and the two status dimensions, making it 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 simple zero-parameter status tool, the description is adequate but not complete. It names what status covers (population and freshness) but does not describe return format, update semantics, or potential error conditions, especially given the lack of an output schema.
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 tool has zero parameters and an empty input schema, so parameter documentation is irrelevant. Baseline for zero-parameter tools is 4, and the description does not need to compensate for missing parameter schema.
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?
Description clearly states the tool returns status information about xOmniMarket source population and freshness. The verb 'return' is specific enough, but it does not explicitly contrast with sibling search_market, leaving some differentiation to inference.
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?
No guidance is provided about when to use this tool instead of search_market or when not to use it. The intended context is implied by the name and description, but there is no explicit usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_marketAInspectSearch qualifying AI capabilities across AWS Marketplace and the Official MCP Registry. Returns both sources by default.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| source | No | ||
| perSource | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the tool searches two sources and returns results from both by default. It does not disclose return format, pagination, rate limits, or whether search is case-sensitive, but for a straightforward search tool this is reasonable. No contradictions with annotations since none exist.
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 only two short sentences, free of filler or repetition. It front-loads the core purpose and then adds the critical detail about default dual-source behavior. No wasted words.
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 3 parameters, no output schema, and no annotations, the description provides the essential purpose and default behavior. It does not explain return values (no output schema exists, so the description could be more helpful there), but for a search tool with enum-based filtering, this is largely complete. The sibling context adds marginal completeness.
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?
Despite having 0% schema description coverage, the description adds meaning beyond the input schema by stating the tool searches for 'qualifying AI capabilities' and notes the default behavior of returning results from both sources. This implies meaning for the 'text' and 'source' parameters. However, it does not explicitly describe the 'perSource' parameter meaning beyond what is already in the schema (integer, max 25, min 1).
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 'Search' and clearly identifies the resource as 'qualifying AI capabilities across AWS Marketplace and the Official MCP Registry'. It explicitly states the default behavior of returning results from both sources, which distinguishes it from siblings like 'market_status' that presumably checks service status rather than performs searches.
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 implicitly states that the tool searches both sources by default, and the 'source' parameter allows narrowing to either 'aws' or 'mcp'. However, it provides no guidance on when to use 'market_status' instead, nor does it explicitly exclude scenarios where this tool is inappropriate.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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