list_seedsList the seeds of the active project — the request sequences that set up and tear down test data. Pass seedId to get one seed with its full step lists and rowVersion. Requires project context.
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | Number of seeds to skip (for pagination, default 0) | |
| take | No | Number of seeds to return (default 100, max 100) | |
| seedId | No | Public Id (Guid) of a single seed. When given, returns that seed with its steps. |
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 readOnlyHint annotation already signals safety, and the description adds that it requires project context and returns full step lists and rowVersion. No destructive side effects are implied, and no contradictions exist.
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 extremely concise — two sentences — and front-loads the purpose. Every word adds value, with no fluff or 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 simple list tool with no output schema, the description covers the core behavior (list/filter seeds), the required context, and what the response includes (step lists, rowVersion). It is complete for an agent to decide and invoke correctly.
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?
All three parameters have descriptions in the schema (skip, take, seedId), and the description adds semantics for seedId (returns a single seed) and implicitly for skip/take (pagination via defaults and max). 100% schema coverage plus meaningful prose.
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 seeds of the active project, defines seeds as request sequences for test data setup/teardown, and distinguishes it from the many sibling list_ tools. The optional seedId filter is also mentioned.
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 says 'Pass seedId to get one seed' and 'Requires project context', giving practical usage hints. It doesn't explicitly compare to alternatives, but the purpose is clear enough to avoid confusion with other list_ tools.
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.