list_team_projectsList the projects a team is assigned to and with which role (read or write). Unlike list_teams this only needs organization membership. Requires organization context. Read-only: members, roles, teams and SSO are not changeable through MCP by design — a human does those in the web app.
| Name | Required | Description | Default |
|---|---|---|---|
| teamId | Yes | Public ID (GUID) of the team |
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?
The annotations already indicate readOnlyHint=true, and the description reinforces this by stating 'Read-only: members, roles, teams and SSO are not changeable through MCP by design — a human does those in the web app.' This goes beyond the annotation to explain why and what is not changeable.
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 three sentences, covering purpose, a contrast to a sibling tool, a requirement, and read-only behavior. It is slightly verbose but each sentence adds value; no redundancy is present.
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?
The description explains what is returned (projects and roles) and mentions the organizational context requirement. Although there is no output schema, the description gives a solid mental model of the tool's behavior and output, but lacks details like pagination or error handling, which are not necessary for a simple list 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 schema fully describes the only parameter teamId as 'Public ID (GUID) of the team'. The description does not add any additional meaning or constraints beyond the schema, so the baseline score 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 lists projects assigned to a team and includes the role (read or write). It also distinguishes this tool from list_teams, providing a precise scope of what the tool does.
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 notes 'Unlike list_teams this only needs organization membership' and 'Requires organization context', giving clear guidance on when to use this tool versus the sibling list_teams. It also clarifies that it is read-only and changes happen in the web app.
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.
The tools are mostly distinct with clear descriptions. Some pairs like get_header_policies vs get_resolved_headers or get_environment_verification vs get_monitoring_sync_status could be slightly confusing, but the descriptions clarify scope and purpose.
All tools follow a consistent snake_case verb_noun pattern (get_, list_, create_, update_, manage_, etc.). Even the few bare verbs like 'search' and 'set_context' are consistent with the naming scheme.
With 165 tools, the server is extremely heavy. This far exceeds the 'too many' threshold of 25+, making it difficult for an agent to navigate and select the right tool efficiently.
The tool surface covers a very broad API lifecycle domain: specs, environments, test cases, monitors, mock servers, security, governance, documentation, and team management. Read and write operations are present across most areas, with no obvious missing core functionality.