The MCP Census
Server Details
Vet any MCP server before you depend on it: live health from GitHub, npm and PyPI.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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.
Tool Definition Quality
Average 4.2/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one for exact lookup by name, the other for keyword search. There is no overlap in functionality.
Both tools follow the same 'census_<verb>' pattern ('lookup' and 'search'), which is predictable and consistent.
With only two tools, the set is minimal but appropriate for a focused census service covering lookup and search. However, it borders on feeling thin.
The tools cover the core operations (lookup and search), but lack additional features like listing all servers or retrieving detailed statistics, which are minor gaps.
Available Tools
2 toolscensus_lookupAInspect
Get the live health verdict for one MCP server by its exact registry name (e.g. 'io.github.owner/name'). Returns stars, last-push recency, gone/archived/deprecated flags, name-collision count, and a fact-based health verdict (healthy | issues | unknown).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Exact MCP registry server name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It details return fields (stars, recency, flags, collision count, health verdict) and implies read-only operation. However, does not disclose potential errors, rate limits, or authentication 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?
Single, well-structured sentence with no waste. Lists return fields efficiently without 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 lookup tool without output schema, description adequately enumerates all return components and their nature ('fact-based health verdict'). Missing only minor details like response format (JSON) or potential null cases.
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 covers the single parameter well (100% coverage), but description adds value by providing an example format ('io.github.owner/name'), clarifying the expected naming convention beyond the schema's 'exact name'.
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 specifies verb ('Get'), resource ('live health verdict for one MCP server'), and requirement for exact registry name. It distinguishes from sibling 'census_search' by emphasizing exact match vs search.
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 when you have the exact server name, contrasting with sibling 'census_search' which likely handles partial names. Lacks explicit when-not-to-use or alternatives beyond the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
census_searchAInspect
Search MCP servers by keyword or partial name (e.g. 'github', 'postgres'). Returns up to 20 matches ranked by GitHub stars, each with its health verdict. Use before choosing an MCP server to depend on.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keyword or partial server name (>= 2 chars) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses key behaviors: returns up to 20 results, ranking by GitHub stars, includes health verdict. No contradictions with missing annotations.
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?
Two sentences, front-loaded with action and results, no fluff. The second sentence adds a clear usage hint. Every word earns its place.
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 search tool with one parameter and no output schema, the description covers purpose, behavior, and usage context adequately. Could mention pagination or rate limits, but not critical.
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% with parameter 'query' already described as 'Keyword or partial server name (>= 2 chars)'. The tool description provides examples but no additional semantics beyond the 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?
The description clearly states the tool searches MCP servers by keyword or partial name, returns up to 20 matches ranked by GitHub stars with health verdict, and distinguishes its role from a sibling by advising use before choosing an MCP server.
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 gives explicit context: 'Use before choosing an MCP server to depend on.' It does not directly compare with the sibling census_lookup, but the use case is clear enough for an agent to know when to invoke this tool.
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.
Discussions
No comments yet. Be the first to start the discussion!