Retrieve plan features and current usage limits for your API key.
Use when:
You want to know how many records/jobs/monitors your plan allows.
You want to check current usage against plan limits before running a large job.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It indicates a read operation ('Retrieve') and mentions optional API key, but could add details like no side effects, cost, or rate limits.
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?
Two sentences, well-structured with a purpose statement and bulleted usage conditions. Every part adds value, no redundancy.
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?
Simple tool with one optional parameter and output schema present. Description fully explains functionality and usage context, sufficient for agent to invoke correctly.
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 coverage is 100% (api_key parameter described fully in schema). The tool description adds no new meaning beyond the schema's parameter description.
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 uses a specific verb and resource: 'Retrieve plan features and current usage limits for your API key.' This clearly distinguishes it from sibling tools, none of which suggest retrieving limits.
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 'Use when' section provides explicit scenarios: knowing plan allowances and checking usage before large jobs. This gives clear context and implicitly advises against misuse.
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.
Each tool is scoped to a specific resource type and action, with clear distinctions between similarly named operations (e.g., pull_results vs pull_job_csv, initialize_query vs validate_query). No two tools appear to perform the same function.
Tools consistently use snake_case verb_noun patterns (create_X, get_X, list_X, update_X, delete_X), with domain-specific verbs like submit, pull, initialize, and validate adding semantic clarity. Minor deviations such as pull_* vs get_* and compound names like create_dataset_from_csv are still predictable.
At 60 tools, the server is heavily overstuffed for a single MCP surface. While the broad domain (datasets, entities, jobs, monitors, projects, webhooks) justifies many operations, the sheer volume exceeds typical recommended limits and includes near-duplicates (pull_results vs pull_job_csv, get_dataset vs get_dataset_status), making agent tool selection unwieldy.
The tool set provides robust CRUD and lifecycle coverage for all major resources, including special operations like csv import, webhook mapping, and monitor enable/disable. Minor gaps such as the absence of a get_monitor (single monitor details) and no cancel_job can be worked around via list_monitors and waiting for job completion.