swarme_account_balanceReturn account credit, metered usage, and API client spend-limit status for the authenticated Swarme machine client.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It clearly indicates a read operation that returns data and scopes it to the authenticated machine client, implying an authentication precondition. It does not mention error cases, rate limits, or whether the call itself affects metered usage, but for a simple balance read the core behavior is transparent enough.
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, front-loaded sentence with no filler. Every clause adds information: the resource, the data categories returned, and the scope of the data. It is appropriately sized for a parameterless read tool.
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?
With zero parameters and an output schema present, the description covers the essential invocation context: the resource, the data categories, and the authentication scope. Nothing an agent needs to issue this call correctly is missing.
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 is empty, so parameter semantics are trivially satisfied. The description adds value by clarifying what the returned data covers – credit, metered usage, and spend-limit status – beyond what the empty schema can convey.
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 ('Return') and a clear resource ('Swarme account balance'), and enumerates exactly what is included (credit, metered usage, spend-limit status). This distinguishes it from sibling status tools like swarme_ai_status and swarme_tool_status, which focus on service or tool status rather than account balance.
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 implies the tool's use case – reading the authenticated client's balance – but gives no explicit guidance about when to prefer it over sibling read/status tools, nor any exclusions. The 'authenticated Swarme machine client' phrase provides some scope context, placing this at the implied-usage level rather than no guidance at all.
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.
Tools are grouped into distinct resource families (account, AI, capabilities, discover, experiments, tool runs, uploads, vault), so most are easy to tell apart. However, multiple 'status' tools and the two Vault metadata tools (documents and summary) could be confused without reading the descriptions carefully.
All tools share a consistent swarme_ prefix and snake_case style, making them predictable. The pattern is not uniformly verb_noun, and there is a plural mismatch between swarme_capabilities_search and swarme_capability_describe, but the naming is generally coherent.
22 tools is on the heavy side and above the typical 3-15 range for a focused MCP server. While the platform covers many domains and each tool has a role, several status-related tools could have been consolidated.
Core workflows like tool runs, experiments, AI model jobs, and Vault fills are well covered. Notable gaps remain: no cancellation for AI jobs or experiments, no listing endpoints for runs/experiments, no Vault field read/delete, and the upload session has no companion completion/status tool.