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delete_project

Delete a project.

By default the project's resources (jobs, monitors, etc.) are detached but kept. Set delete_resources=true to also delete the contained jobs, monitors, datasets, and monitor groups. Webhooks are the exception: they are never deleted by this operation — an attached webhook is only detached from the project and keeps working (it may belong to other projects or resources independently of this one).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
project_idYesThe project ID to delete.
delete_resourcesNoIf true, also delete the project's resources except webhooks, which are always detached rather than deleted (default false).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full responsibility for transparency. It discloses that resources are detached but kept by default, that delete_resources=true deletes them except webhooks, and that webhooks remain attached and functional. This exceeds typical destructive-tool descriptions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and uses compact, information-dense sentences. Each detail has relevance, from default detachment to webhook exception.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a destroy action with output schema present, the description fully explains scope and exceptions. It covers the project, resources, the flag, and webhook edge case, making the tool's behavior predictable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully documents parameters (100% coverage). The description nonetheless adds meaningful semantics by explaining the default vs. flag behavior and the webhook exception, which clarifies the delete_resources parameter's implications beyond its schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Delete a project,' a clear verb+resource statement. It distinguishes from sibling delete_* tools by explicitly scoping to projects and detailing the treatment of contained resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on default behavior and the delete_resources flag, and explicitly notes webhooks are never deleted. It doesn't name alternative tools for deleting specific resources, but the project-scoped intent is clear.

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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TDQS

A3.6/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness5/5

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.