updateConnectionUpdate an existing connection. For sensitive header values, sending an empty string keeps the existing value; send a new value to overwrite. Toggling shared moves the connection between personal and team-shared.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| shared | No | ||
| headers | No | ||
| server_url | No | ||
| auth_method | No | ||
| 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?
With only generic annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=false), the description adds meaningful behavioral detail: empty strings preserve sensitive header values and toggling `shared` moves between personal and team-shared. This goes beyond the schema and gives agents important update semantics, though it doesn't disclose all edge cases like how to remove a header.
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?
Three concise sentences: the first states purpose, the second clarifies sensitive-header behavior, and the third clarifies the `shared` toggle. Every sentence adds necessary information, and the most important nuance is front-loaded immediately after the purpose statement.
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?
For a mutating tool with six parameters and no output schema, the description covers the two most non-obvious behaviors (partial header updates and `shared` toggling) well. It is slightly incomplete in not stating whether omitted fields are left unchanged or how to clear sensitive header values, but overall it provides enough context for correct invocation in most cases.
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 only 17%, so the description partially compensates by explaining the nuanced behavior of `headers` and `shared`. Other parameters like `name`, `server_url`, and `auth_method` rely on their self-explanatory names and enum/format constraints, but the description does not describe every parameter's update behavior.
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 begins with 'Update an existing connection,' a specific verb and resource that clearly distinguishes this from createConnection, deleteConnection, getConnection, and listConnections. The word 'existing' reinforces the update semantics and makes the tool's purpose unambiguous.
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 clearly implies this is the tool to modify an already-created connection, which is the right context against the sibling create/delete/get/list tools. However, it does not explicitly state when not to use it or name an alternative tool, leaving some routing 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.