get_catalog_statsAggregate health of the whole MCP population: verdict breakdown, share of probeable servers actually serving, transport mix, handshake latency percentiles, tool counts and probe freshness. This is the 'how healthy is MCP right now?' headline number.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Annotations already mark it as read-only. The description adds substantial behavioral context by detailing exactly what metrics are returned (handshake latency percentiles, tool counts, probe freshness), which is beyond what annotations provide.
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 with zero wasted words. The first sentence lists the key data points, and the second provides a clear tagline. Perfectly front-loaded and efficient.
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 no output schema, the description compensates by enumerating the return contents comprehensively. The tool is simple (aggregate stats, no parameters), and the description fully covers what an agent needs to know.
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 no parameters, so the input schema is fully covered. The description does not need to add parameter information, and it does not repeat schema details. Baseline 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 clearly states the tool aggregates health stats for the whole MCP population and lists specific metrics (verdict breakdown, serving share, etc.). It distinguishes itself from sibling tools like get_mcp_server or get_server_history which operate on individual servers.
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 explicitly frames the output as a headline number for overall health, implying when to use it. It does not explicitly list alternatives or conditions to avoid, but the purpose is clear enough for an agent to make a correct selection.
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.
Each tool has a clearly distinct purpose: catalog stats, individual server entry, history, status, categories, schema drift, incidents, and search. No overlap in functionality.
All tools follow a consistent verb_noun pattern (get_, list_, search_) using snake_case, making it predictable for agents.
With 8 tools, the set is well-scoped for a monitoring/catalog service—neither too sparse nor bloated, each tool adds clear value.
Covers core monitoring lifecycle (stats, server details, status, history, incidents, search). Slight gap: no direct 'list all servers' endpoint, but search can approximate it.