getConnectionCredentialsGet the header keys configured for a connection. Sensitive values (api keys, tokens, passwords) are returned as empty strings — the response only reveals which credential fields are set, never their values.
| Name | Required | Description | Default |
|---|---|---|---|
| connection_id | Yes | Connection ID |
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?
The description adds significant behavioral transparency by stating that sensitive values are returned as empty strings, revealing only which fields are set. This goes beyond the readOnlyHint annotation by explaining the exact output behavior. It also implies no side effects, consistent with the idempotent and non-destructive annotations.
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, concise sentence that efficiently conveys the purpose and key behavior. It avoids unnecessary details and is well-structured, making it easy for an agent to parse and understand the tool's functionality.
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?
The description is fairly complete for its scope, explaining both the output (header keys) and the masking of sensitive values. However, it does not mention related tools or provide guidance on when to choose this over getConnection or getCredential, which could be beneficial context. Still, the core information needed for usage is present.
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 schema fully describes the parameter with a 'Connection ID' description, and the tool description does not add further semantic detail. The parameter is a straightforward identifier, and the description provides no additional context about how it is used. Since schema coverage is 100%, the baseline score of 3 is appropriate.
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 purpose: to get the header keys configured for a connection. It explicitly distinguishes itself by noting that sensitive values are masked, which differentiates it from tools like getConnection that might return full details. The verb 'get' is specific and the resource 'connection' is identified.
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 does not explicitly state when to use this tool over alternatives like getConnection or getCredential. It implies usage when only header key names are needed, but does not provide explicit guidance or exclusion criteria. The behavioral note about masking sensitive values gives some context, but no direct comparison to sibling 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.
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.