Return measurement probes for agentic workflow traceability and readiness.
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | ||
| class_id | No | ||
| decision | No | ||
| probe_id | No | ||
| workflow_id | No | ||
| minimum_score | 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?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. The verb 'Return' strongly implies a read-only operation, but the description does not explicitly state that it is non-mutating, nor does it mention any rate limits, authentication requirements, or side effects. It also does not describe what happens with the various parameters (e.g., filtering behavior) or the nature of the returned data. This is a minimal disclosure that leaves the agent guessing about operational details.
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 with no wasted words, which is concise in a structural sense. However, it is under-specified: it lacks essential information and reads as a placeholder rather than a helpful guideline. It is not front-loaded with the most critical details, and the brevity comes at the cost of informativeness. It earns a middle score because it is not verbose, but it fails to provide necessary content.
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 6 parameters, no annotations, no output schema explanation (though it has an output schema), and a large family of sibling tools, the description is severely incomplete. It does not explain the meaning of the parameters, the nature of the returned probes, or how this tool fits into the broader agentic workflow measurement context. An agent would struggle to use this tool correctly without additional external knowledge, making the description inadequate for its context.
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%, meaning the description must compensate by explaining the six parameters (status, class_id, decision, probe_id, workflow_id, minimum_score). It does not mention any of them, nor does it provide hints about their purpose or how they influence the returned probes. The description adds no value beyond what the schema already shows (which is just type information with null defaults). This is a critical gap for an agent needing to construct a valid call.
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 action (return) and a resource (measurement probes) with a domain qualifier (agentic workflow traceability and readiness), which gives a general idea of the tool's function. However, it does not explain what 'measurement probes' are or how this tool differs from the many sibling 'pack' tools, such as recipes_agentic_assurance_pack or recipes_agentic_run_receipt_pack. The purpose is clear enough to avoid total confusion but lacks the specificity needed to distinguish it from alternatives.
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 gives no guidance on when to use this tool versus any of the large sibling pack tools. It does not mention any conditions, prerequisites, or scenarios where this tool is preferred. An agent would have to infer the usage solely from the name and the generic purpose statement, which is insufficient given the abundance of similar 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.
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.