Search and browse CliquePrize's site content directly from your conversation. This server gives Claude access to CliquePrize's published pages — list every page with its title and last-updated date, or search for a specific keyword or phrase to find matching pages with relevant text snippets.
Tools:
list_pages — List all published CliquePrize site pages with slug, title, and last-updated date search_content — Search CliquePrize site content for a keyword or phrase, returning matching pages wit
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: list_pages enumerates all published pages, while search_content finds pages matching a keyword with snippets. There is no overlap or ambiguity between them.
Both tools follow the verb_noun pattern consistently: list_pages and search_content. The naming is predictable and easy to infer.
With only two tools, the server feels thin and borderline appropriate. The tools are useful for read-only discovery, but the low count suggests an intentionally narrow scope.
The tool surface only supports listing and searching pages; there is no way to retrieve a full page by slug or perform any content management operations. This creates a significant dead end for agents needing page content beyond snippets.
list_pagesAInspectList all published CliquePrize site pages with slug, title, and last-updated date
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full burden. It clearly states the output fields, which offers some transparency about the read-only nature. However, it does not disclose potential pagination, sorting, or any rate limits, and the 'published' filter is mentioned without further qualification.
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 that front-loads the verb and object. Every word earns its place, and there is zero redundancy.
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 no-parameter, read-only list operation, the description is adequate and complete. It states the source, scope, and output fields. The only minor gap is not mentioning any default ordering or potential pagination, but these are not critical for a simple list with no parameters.
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?
There are zero parameters, and the schema coverage is 100%. The description adds value by clarifying the output fields, which partially compensates for the lack of parameters. Since there are no parameters to describe, the baseline of 4 is appropriate.
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 the specific verb 'List' and clearly identifies the resource as 'all published CliquePrize site pages', along with the exact fields returned ('slug, title, and last-updated date'). This makes the tool's purpose unambiguous and distinct from the sibling 'search_content', which implies filtering.
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 on when to use this tool versus the sibling 'search_content'. The description implies its use for listing all pages, but there is no explicit mention of when to prefer this over search or any exclusions/caveats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_contentAInspectSearch CliquePrize site content for a keyword or phrase, returning matching pages with a text snippet
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It does disclose the return behavior ('matching pages with a text snippet'), which is useful, but it does not mention ordering, limits, exact-match semantics, or whether the operation is read-only. This is moderate transparency for a simple search tool.
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, well-structured sentence with no filler. It front-loads the action and resource, then states the output format. Every word contributes to understanding the tool.
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 search tool with no output schema or annotations, the description covers the core purpose and return value ('text snippet'). It is missing a direct comparison to 'list_pages,' but the low complexity means the description is largely sufficient.
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 provides only the parameter name 'query' with a minLength constraint and no description. The description compensates by clarifying the parameter means 'a keyword or phrase,' adding semantic meaning beyond the raw schema. It does not specify formatting or additional semantics, but for a single parameter this is reasonable.
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 specifies a clear action ('Search'), a distinct resource ('CliquePrize site content'), and the result ('returning matching pages with a text snippet'). It effectively distinguishes from the sibling tool 'list_pages' by emphasizing keyword/phrase-based search rather than enumeration.
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 when to use the tool: when you need to find content by keyword or phrase. However, it does not explicitly state when not to use it or name 'list_pages' as an alternative for browsing all pages, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
