Check the status of a batch run. Returns progress and output file URLs when completed.
| Name | Required | Description | Default |
|---|---|---|---|
| batch_run_id | Yes | The batch run identifier returned by run_batch. |
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 declare readOnlyHint=true and idempotentHint=true, indicating a safe read operation. The description adds valuable context by specifying that it 'Returns progress and output file URLs when completed,' which is not derivable from the annotations or schema. This enriches the agent's understanding of the tool's behavior beyond the structured hints.
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 extremely concise: two sentences with no filler. It front-loads the core purpose and then adds a valuable detail about return values. Every word earns its place, making it an example of efficient, well-structured documentation.
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 read-only status tool with one parameter and no output schema, the description covers the essential elements: what it does and what it returns. It lacks explicit guidance on alternatives (e.g., get_flow_status for flows), but given the low complexity and strong annotations, the description is sufficiently complete. A near-perfect score is not warranted because it does not mention potential error conditions or polling behavior.
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 fully describes the single parameter 'batch_run_id' with a clear reference to run_batch, achieving 100% schema coverage. The description does not add any additional parameter-level meaning, so the baseline of 3 is appropriate. The tool description correctly ties the parameter to the batch run concept, but the schema already handles the semantics.
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: 'Check the status of a batch run.' It specifies the verb ('check'), the resource ('batch run'), and distinguishes it from sibling tools like get_flow_status by focusing on batch runs rather than flows. The added detail about returning progress and output file URLs further clarifies what is being retrieved.
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 usage context: after running a batch via run_batch, this tool checks its status. It provides clear context for when to use the tool, but it does not explicitly mention alternatives or when-not-to-use scenarios. Since the purpose is self-evident from the name and description, a 4 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct resource and action, with clear separation between flow lifecycle, execution, model exploration, community, and account tools. Even similar-sounding tools like create_flow, preview_flow, and suggest_flow have clearly different purposes (actually creating, dry-running, and recommending models). Descriptions prevent misselection.
All tools follow a consistent verb_noun pattern in snake_case (e.g., create_flow, list_flows, run_batch, cancel_flow). No mixed conventions or vague verbs like 'process' or 'handle'. The naming is uniform and predictable.
At 33 tools, this is a large surface, but each tool addresses a distinct feature of the cnaps.ai platform, from flow CRUD and execution to community features and notifications. Still, it exceeds the typical well-scoped range and feels heavy, making it a borderline case between appropriate and too many.
The core flow lifecycle (create, read, update, delete, restore, duplicate) and execution (run, batch, cancel) are covered, but structural editing of flow graphs is missing—update_flow only changes parameters, not topology. Additionally, there is no run history, batch list/cancel, or community post update/delete, leaving notable gaps for a platform API.