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remove_project_resource

Remove a single resource from a project.

This detaches the resource from the project without deleting the resource itself (e.g. removing a webhook only ends its membership in this project; the webhook keeps existing and stays attached to any other projects).

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 remove the resource from.
resource_idYesThe ID of the resource to remove.
resource_typeYesResource type: 'job', 'monitor', 'dataset', 'monitor_group', or 'webhook'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It clearly states the non-destructive nature ('without deleting the resource itself') and gives an example that reinforces this. It does not mention permissions or error states, but the core behavioral nuance is well covered.

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 two sentences, front-loaded with a clear command, and includes a valuable example that clarifies the behavior. No redundant or unnecessary information.

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 simple tool with full schema documentation and an output schema, the description provides the essential behavioral context and example usage. Edge cases like error handling are not necessary for this scope, making the overall package complete.

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

Parameters3/5

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

Schema coverage is 100%, so all parameters are already fully described in the schema. The description adds a small semantic hint with the webhook example for resource_type, but no additional parameter-level details beyond what the schema provides. Baseline 3 is appropriate.

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 uses a specific verb ('Remove') and resource ('resource from a project'), and clarifies scope ('single resource'). It distinguishes from sibling add_project_resources by emphasizing 'single' and from delete_* tools by explicitly stating the resource is not deleted.

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 clearly indicates when to use the tool: when you want to detach a resource from a project without deleting it, with a concrete webhook example. It does not explicitly name alternatives, but the contrast with deletion is evident, providing sufficient context for a simple operation.

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.