ChatGPT-compatible read-only fetch alias. Returns a full MCP server item with id, title, text, url, and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Canonical MCP server identifier in namespace/name format. |
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 declare readOnlyHint=true, and the description reinforces this with 'read-only.' It adds the return shape (id, title, text, url, metadata) and the 'ChatGPT-compatible' tag, but does not cover error behavior, side effects, or prerequisites. This adds some context beyond annotations but is not especially rich.
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 sentence and front-loaded with the core purpose. It is concise and doesn't waste words, though the 'ChatGPT-compatible' label could be clearer.
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 read-only fetch, the description covers the purpose and return format sufficiently. The output schema is absent, so listing the return fields is helpful. It doesn't address error cases, but annotations handle the safety profile and the tool is simple.
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 id parameter is fully documented in the schema with format guidance ('namespace/name format'), so the description adds little extra semantic value. With 100% schema coverage, the baseline of 3 applies.
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 fetches a full MCP server item and lists the returned fields (id, title, text, url, metadata). It distinguishes itself from siblings by specifying the resource type, though it doesn't explicitly compare to other getter tools like get_server_report or get_agent.
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 alternatives. The phrase 'ChatGPT-compatible' hints at a context but is not explained, and there are no exclusions or 'use this instead of' statements.
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.
Most tools target distinct resources and actions (e.g., get_agent vs. get_drift_report). A few pairs like search/search_servers and fetch/get_server_report overlap in intent, but descriptions clarify differences in output and purpose. Agents may need to read carefully but can generally tell tools apart.
Nearly all tool names follow a verb_noun snake_case pattern (list_agents, create_mandate, export_policy). The bare aliases 'fetch' and 'search' deviate slightly, but are clearly intentional, and there is no mix of camelCase or other styles. Overall consistent and predictable.
22 tools is on the heavy side, falling into the '16-25' borderline range. While each tool appears to serve a distinct function, the set could be consolidated (e.g., the multiple 'get_*_options' tools). It is not excessive enough to be chaotic, but it is above the ideal 3-15 range.
The tool surface covers core workflows for server search, comparison, report generation, policy export, mandate creation, agent risk management, and decision-making. Minor gaps exist, such as no create/update/delete for most resources and no subscription creation, but the domain is read/decision-heavy and core lifecycle coverage is adequate.