fastero_read_notebookRead a notebook’s cells in order: code (Python, or SQL whose result becomes a DataFrame) and markdown, each code cell with the output saved from its last run. Nothing is run, and saved output can be large. fastero_write_notebook replaces the cells.
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | Yes | The project’s id, from fastero_list_projects. | |
| notebookId | Yes | A notebook’s id, from fastero_list_notebooks. |
Changes observed during successful MCP inspections.
Input schema / properties / notebookId / descriptionAdded value: +"A notebook’s id, from fastero_list_notebooks."Input schema / properties / projectId / descriptionAdded value: +"The project’s id, from fastero_list_projects."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful non-obvious behavior: no execution occurs, output reflects the last run, and payloads may be large. It stops short of describing pagination or truncation handling for the 'large' output warning.
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 tight sentences, front-loaded with what is read, then behavior (nothing run, output large), then the alternative tool. No filler and every sentence carries distinct information.
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 no output schema, the description does the work of explaining the return shape (ordered cells, types, saved outputs), which is enough for an agent to know what to expect. It could say a bit more about output size limits or how large outputs are surfaced, but it is essentially complete for a two-param read 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% and both parameters (projectId, notebookId) are documented in the schema, including their source tools. The description adds no additional parameter meaning, so the baseline of 3 is appropriate.
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?
States a specific verb (Read) and resource (a notebook's cells), and details what is returned: code cells (Python or SQL-to-DataFrame) and markdown, each code cell with its last saved output. It also names the sibling fastero_write_notebook, so an agent can distinguish read from write without opening either schema.
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?
Gives a clear usage context – 'Nothing is run' and 'saved output can be large' tell the agent this is a static snapshot, not an execution. It names fastero_write_notebook as the mutation alternative, though it doesn't explicitly contrast with other read siblings like fastero_get_notebook_run or fastero_read_python_file.
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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