Update a dataset's name and/or description.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Optional new dataset name. | |
| api_key | No | CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var. | |
| dataset_id | Yes | The dataset ID to update. | |
| description | No | Optional new dataset description. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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?
No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as idempotency, authorization requirements, rate limits, side effects (e.g., overwriting existing values), or response behavior. For a mutation tool, this is insufficient.
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, front-loaded with the key action. It contains no waste, but the extreme brevity sacrifices necessary context, making it less effective than a slightly longer but more informative version.
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 tool has 4 parameters (1 required) and an output schema, yet the description provides no additional context about return values, error states, or typical usage patterns. Given that the schema already describes parameters, the description should fill gaps, but it leaves many behavioral questions unanswered.
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 has 100% description coverage for all 4 parameters. The description adds minimal value beyond summarizing that name and/or description can be updated, which is already evident from the parameter names and schema descriptions.
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 verb 'Update' and the resource 'dataset', specifying the fields that can be changed ('name and/or description'). This directly differentiates from sibling tools like create_dataset, delete_dataset, and get_dataset.
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 offers no guidance on when to use this tool versus alternatives, no prerequisites, no error conditions, and no indication of scope or limitations. It simply states what the tool does without context.
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.
Most tools have distinct purposes, but some pairs like create_dataset vs create_dataset_from_csv or pull_results vs pull_job_csv could cause confusion. However, descriptions clarify differences.
Tools follow a consistent verb_noun pattern (e.g., create_dataset, list_datasets) with minor exceptions like append_csv_to_dataset and pull_job_csv. Overall predictable.
60 tools is high for an MCP server, but the domain (web research, job processing, multiple resource types) justifies the count. Still borders on excessive.
The server offers full CRUD for datasets, entities, monitors, projects, webhooks, plus job submission, status polling, result retrieval (JSON/CSV), webhook management, and health endpoints. No obvious gaps.