Return context egress data classes, destination classes, and workflow boundary policy.
| Name | Required | Description | Default |
|---|---|---|---|
| source_id | No | ||
| data_class | No | ||
| workflow_id | No | ||
| destination_class | No |
| 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?
With no annotations provided, the description carries the full responsibility for behavioral disclosure. It only says 'Return', implying a read operation, but does not state whether it is read-only, what side effects occur, or how parameters modify the output. No behavioral traits beyond the name are disclosed.
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, concise and front-loaded with the verb 'Return'. However, it is so minimal that it lacks structure—the three output types are listed without separation or context, making it less scannable than it could be.
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 the tool has 4 optional parameters and no schema descriptions, the description is inadequate. It does not explain the purpose of each parameter or how they are used. The existence of an output schema helps, but the description still leaves the agent guessing about call semantics and expected usage.
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 description coverage is 0%, so the description must explain parameters. It mentions 'data classes' and 'destination classes' but does not map them to the four parameters (source_id, data_class, workflow_id, destination_class). It fails to clarify what each parameter does or how they filter results.
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 states a specific verb 'Return' and lists three resource types (context egress data classes, destination classes, workflow boundary policy), which clearly explains the tool's output. However, it does not differentiate from numerous sibling 'pack' tools that also return policies or classes, so it is not fully distinctive.
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 description does not mention conditions for use, exclusions, or any reference to sibling tools, leaving the agent to infer the appropriate context.
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.
Many tools return 'pack' artifacts with nearly identical descriptions, such as recipes_agentic_assurance_pack, recipes_agentic_posture_snapshot, and recipes_agentic_readiness_scorecard, or recipes_mcp_connector_intake_pack versus recipes_mcp_connector_trust_pack. Distinct domains like CVE lookup and playbooks are clear, but dozens of evidence/profile packs blur together and will cause misselection.
All names use the recipes_ prefix and snake_case, and most pack tools follow a [domain]_[topic]_pack pattern, which aids recognition. However, verbs are placed inconsistently and mixed with noun-only names: recipes_get, recipes_cve_get, recipes_mcp_server_get, recipes_refresh, and many pure 'pack' names.
Seventy-five tools is an extreme count for any MCP server, especially when the majority are highly specialized 'pack' endpoints with narrow outputs. The sheer number creates major selection overhead and makes the tool surface difficult for an agent to navigate reliably.
The server covers its apparent read-only scope thoroughly: recipe search/get, CVE lookup, playbook planning, MCP server catalog, upstream MCP introspection, and extensive evidence packs. There are no obvious dead ends, though the massive pack proliferation makes it harder for agents to know which tool to call.