Return the secure context value model for buyer, ROI, and acquisition diligence.
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | ||
| wedge_id | No | ||
| driver_id | No | ||
| segment_id | No | ||
| question_id | No | ||
| scenario_id | 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?
Because no annotations are provided, the description carries the full burden of behavioral disclosure. 'Return' implies a read-only operation, but the description does not explain whether this is a template, a filtered query, or a generated model, nor does it disclose side effects, matching behavior, or any other runtime traits.
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, front-loaded sentence with no filler. While it is terse and could convey more, every word contributes to the purpose statement and the structure is clean.
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?
With six optional parameters, no annotations, and minimal description, an agent lacks enough context to invoke the tool confidently. The output schema covers return structure, but parameter semantics and behavioral expectations are left unexplained.
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% and the description does not mention any of the six optional parameters. The names such as wedge_id, driver_id, and segment_id give hints, but the description fails to explain how these parameters affect the returned value model.
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 uses a specific verb ('Return') and names the resource ('secure context value model') plus its domain ('buyer, ROI, and acquisition diligence'). This makes the primary purpose reasonably clear, though it does not explicitly distinguish the tool from the many sibling secure-context recipe tools.
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?
There is no guidance about when to use this tool versus alternatives, and no exclusions or preferred conditions are stated. The phrase 'for buyer, ROI, and acquisition diligence' gives some context but does not help an agent decide between this and siblings like recipes_secure_context_buyer_diligence_brief or recipes_secure_context_eval_pack.
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.