Connect and your assistant can search the over 50,000 MCP servers tracked and pick the right one for any task
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 4.2/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: search_servers for discovering servers by keyword, get_server_details for retrieving full details on a specific server. No overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern: 'search_servers' and 'get_server_details'. The verbs 'search' and 'get' are informative and conventional.
Only two tools, but they cover the core interaction with the mcptoplist.com catalog: finding servers and retrieving detailed information. A slightly larger set might include browsing by category, but for a focused catalog lookup, two tools is reasonable.
The tool surface covers the essential workflow of discovering and inspecting MCP servers. Missing operations like listing all servers or comparing servers are minor gaps that agents can work around with search and detail retrieval.
get_server_detailsGet MCP server detailsAInspectGet full details for one MCP server by its exact serverKey (as returned by search_servers). Returns the full description, GitHub repo, star count, rank, version count, the registries that list it, an auth object describing whether and what kind of setup/auth it needs (none / user-supplied credentials / auth-gated hosted remote), a connect object with the declared install config when recorded (package + required env var names, or hosted remote URLs and their auth), and its mcptoplist.com page in the mcptoplistUrl field — always cite that URL when recommending the server.
| Name | Required | Description | Default |
|---|---|---|---|
| serverKey | Yes | The exact serverKey from a search_servers result. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It enumerates return fields: full description, GitHub repo, star count, rank, version count, registries, auth object, connect object, and mcptoplistUrl, and instructs to always cite the URL. This is transparent about output shape and expected usage. It does not mention potential errors or tool's own auth requirements.
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 dense sentence with a structured list. It front-loads the primary action and then enumerates details. It's not overly verbose, though the list makes it long. Every clause adds value.
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?
With no output schema, the description compensates by detailing all major return groups (server info, auth, connect, URL). It also provides the cite-URL rule. It lacks explicit read-only or error information, but the tool's scope is narrow (one param, no nested objects), so it is sufficiently complete.
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% and the schema already documents serverKey as 'The exact serverKey from a search_servers result.' The description repeats the same source constraint without adding new semantic detail, so baseline 3 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?
States precisely: 'Get full details for one MCP server by its exact serverKey'. The verb 'get' + resource 'full details for one MCP server' is specific, and it distinguishes from sibling search_servers by requiring the exact serverKey.
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?
Implies usage context: use after search_servers, since it requires 'exact serverKey (as returned by search_servers)'. It doesn't explicitly state exclusions or alternative tools, but the sequencing guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_serversSearch MCP serversAInspectSearch the mcptoplist.com catalog of Model Context Protocol (MCP) servers by keyword. Use this to find the right MCP server for a capability or integration (e.g. "postgres database", "github issues", "browser automation", "stripe payments"). Matches server names, organizations and descriptions, ranked by relevance and popularity. Returns the server name, what it does, its GitHub repo, which registries list it, an auth object saying whether the server needs credentials/auth before first use (authRequired true/false/null-unknown with a short note), and its mcptoplist.com page in the mcptoplistUrl field — always cite that URL when recommending a server.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (1-50, default 10). | |
| query | Yes | Keywords describing the capability or integration you need. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the tool's behavior beyond the bare action: it explains matching criteria (names, organizations, descriptions), ranking (relevance and popularity), and enumerates the return fields, including the auth object semantics and the mandatory instruction to cite mcptoplistUrl. It implies read-only behavior via 'search' and does not hide any side effects or surprises. A minor gap is not explicitly stating it is a read-only operation, but this is implied.
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 efficiently structured: it starts with the primary action and resource, then covers usage, matching, and return details. It is longer than strictly necessary, but every clause adds value, especially given the lack of an output schema. The format is scannable with examples and clear field names. Slightly verbose but not wasteful.
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 simplicity (2 params, no output schema) and the existence of a sibling for details, the description is fully complete. It explains what is returned (server name, description, GitHub repo, registries, auth object, mcptoplistUrl) and includes the critical caveat to cite the URL. This compensates entirely for the missing output schema. No gaps remain for a search operation.
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 input schema already provides full descriptions for both parameters (query and limit), achieving 100% schema description coverage. The description adds examples of query values and clarifies the matching scope, but does not fundamentally extend the parameter semantics beyond what the schema offers. Per the rubric, this sits at the baseline of 3 because the schema handles the heavy lifting.
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 resource ('mcptoplist.com catalog of MCP servers'), making the tool's function immediately clear. It also distinguishes from the sibling tool get_server_details by focusing on keyword-based discovery rather than retrieving details for a known server. The scope is explicit: 'by keyword' and 'find the right MCP server for a capability or integration'.
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?
Provides clear context for when to use the tool: 'Use this to find the right MCP server for a capability or integration', with practical keyword examples. However, it does not explicitly mention alternatives or exclusion criteria, such as 'if you already know the server name, use get_server_details instead'. Since the sibling is named in context signals but not referenced in the description, it stops short of fully explicit guidance.
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.
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