getLegacyClarityProcessGet a legacy v1 Clarity process with process metadata, generated analysis, automation guidance, generation progress, and captures. Use this for rows from listClarityProcesses where version=1; v2 rows should use getClarityProcess and snapshot detail tools.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
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?
Annotations already establish readOnly, idempotent, and non-destructive behavior, so the bar for added transparency is lower. The description adds context that this is a 'legacy' v1 process and lists the output categories, which gives some insight into expected behavior. However, it does not mention potential side effects or limitations beyond the legacy tag, so it's not perfectly transparent.
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 concise, consisting of two sentences that efficiently communicate purpose and usage. It avoids redundancy and fluff, making it straightforward to parse. The structure is logical: first the action and content, then the usage guidance.
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 lack of an output schema, the description does provide a high-level summary of what is returned (metadata, analysis, guidance, progress, captures). It also includes essential usage context for when to apply this tool. However, it does not detail the exact structure of the output or any error conditions, leaving some gaps, but it's sufficient for basic understanding.
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?
The input schema defines only 'id' with no description, and the tool description does not explicitly explain what 'id' represents. While it can be inferred from the context ('rows from listClarityProcesses') that 'id' is the process identifier, this is implicit rather than explicit. With 0% schema description coverage, the tool description should have compensated by clearly stating the parameter's meaning, which it fails to do.
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's action ('Get') and resource ('legacy v1 Clarity process'), enumerates the returned content (metadata, analysis, guidance, progress, captures), and explicitly distinguishes it from the v2 counterpart (getClarityProcess). This leaves no ambiguity about the tool's purpose.
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 provides explicit usage criteria: 'Use this for rows from listClarityProcesses where version=1' and directs v2 rows to alternative tools. This makes the when-to-use and when-not-to-use conditions crystal clear, leaving nothing to inference.
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.
Despite detailed descriptions, many tool names are highly ambiguous, with multiple tools covering the same conceptual actions (e.g., acceptClarityCaptureSuggestion vs. acceptClarityTeamAssignmentSuggestion, or the many deleteClarity*Interview tools). The set is so large that distinguishing between, say, listClarityFolders, listClarityProcesses, and listClarityProcessSummaries requires reading deep into descriptions, reducing agent selection accuracy.
The naming convention is predominantly verb_noun (e.g., createClarityProcess, listAgents, deleteQueue), and is remarkably consistent across the 316 tools. There are only minor deviations, such as 'fileSuggestedClarityProcesses' (verb + adjective noun) and 'bulkUpdateCasePriority' (where 'bulk' could be seen as a prefix), but overall the pattern holds strongly.
With 316 tools, this server is extremely oversized for any single agent to manage effectively. The massive number of tools suggests poor modularization—many of these tools likely belong in separate, smaller servers focused on specific domains (e.g., Clarity, Pulse, Agent management). The cognitive load for an agent to choose from 316 options is very high, leading to frequent misselection.
The tool surface covers an extraordinarily wide range of operations across the Duvo platform: agents, runs, cases, queues, Clarity processes, skills, integrations, notifications, teams, and more. Most resource types have full CRUD and lifecycle management. Notable minor gaps exist (e.g., no tools for managing specific notification batch severities dynamically, and some interview management is missing batch operations), but for the platform's scope, coverage is impressively thorough.