List recipes with optional metadata filtering.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| agent | No | ||
| limit | No | ||
| facets | No | ||
| section | No | ||
| severity | No | ||
| min_quality | 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'List recipes', which implies a read-only operation, but does not disclose pagination, default limits, sorting, or any side effects. There is no explicit statement of safety or side-effect behavior, making it insufficient for an agent to understand the operational profile.
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 brief (six words), but this is under-specification rather than conciseness. The single sentence lacks essential details about filters, sorting, or output, making it inadequate for a tool with seven optional parameters. It is not front-loaded with critical information because no critical information 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?
Given the tool's complexity (seven optional parameters) and the presence of an output schema, the description should explain what filters are available, how they combine, and what the response contains. However, it provides only a generic statement. The description is completely inadequate for an agent to understand how to construct a valid request or interpret the results, especially in a large sibling family where similar tools exist.
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 compensate by explaining parameter usage. It mentions 'optional metadata filtering' but does not map any of the seven parameters (tags, agent, limit, facets, section, severity, min_quality) to their meaning or purpose. The description adds no semantic value beyond what the parameter names themselves imply, which is insufficient for correct usage.
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 clear verb ('List') and resource ('recipes'), and mentions 'optional metadata filtering'. However, it does not differentiate from sibling tools like recipes_search or recipes_get, which also list or retrieve recipes. The term 'metadata filtering' is vague and doesn't specify which metadata fields or how filtering works, leaving ambiguity about its unique role.
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. There is no mention of when not to use it, no examples, and no reference to sibling tools. The description simply states what it does without context on selection criteria.
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.