getFileContentGet the content of a text file.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path of the file relative to the team's storage root | |
| team_id | No | Duvo team UUID to operate on. API keys are pinned to a single team — omit this (it falls back to the key's team) or pass that same team; a different team is rejected. OAuth callers, who can span multiple teams, should pass the target team here. |
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 is consistent with the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) — a read operation — so there is no contradiction. The 'text file' qualifier adds a mild behavioral constraint (binary files aren't supported), but the description otherwise discloses nothing beyond the annotations: no return encoding, no size limits, no missing-file error behavior. With annotations carrying the safety profile, the added value is thin but present.
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?
A single sentence, 'Get the content of a text file,' with zero wasted words and the verb front-loaded. It's appropriately brief for a simple read tool, though this sentence is also the entire description with no supporting structure.
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 simple 2-parameter read tool, the schema covers all parameters and the annotations cover the safety profile, so the essentials are in place. However, with no output schema, the description doesn't specify how content is returned (raw text vs wrapped/encoded payload), and behavior for missing paths or non-text files is unstated — modest but real gaps for an agent deciding whether and how to invoke the tool.
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 100%, so both parameters are already well documented: path is explicitly 'relative to the team's storage root,' and team_id carries detailed API-key-pinning versus OAuth guidance. The tool description itself adds no parameter-level meaning, so the baseline 3 applies.
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 verb ('Get') and resource ('the content of a text file'), making the tool's core function immediately clear. It implicitly distinguishes from siblings like getFileDownloadUrl (URL vs content) and updateFileContent (modify vs read), but it doesn't name alternatives or explicitly call out the text-file-only scope, so it stops short of a 5.
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 on when to use this tool versus alternatives. The description never mentions getFileDownloadUrl for URL needs, updateFileContent for edits, or listFiles for file discovery, and it gives no exclusions for binary files or large files. An agent must infer usage entirely from the tool name and sibling list.
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.