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continue_job

Expand a job by processing more records beyond the initial limit.

This increases the number of records the system processes (which costs additional credits). Only use this when the user wants MORE data processed.

This only applies to jobs originally submitted with limit. If a job was submitted without limit, there is nothing to continue. The new_limit must be greater than the previous limit when provided. If omitted, API defaults to your plan maximum.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job ID to continue processing
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
new_limitNoOptional new record processing limit (must exceed the previous limit if provided).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.6/5.0
Behavior4/5

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

No annotations provided, so description bears full responsibility. It discloses that processing costs additional credits and that omitting new_limit defaults to plan maximum. However, lacks details on job state requirements or side effects, but still adds valuable behavioral context beyond the schema.

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 concise (4 sentences), front-loaded with the main action, and contains no extraneous information. Every sentence serves a purpose: action, cost, applicability, parameter constraint.

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

Completeness4/5

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

Given the presence of an output schema (relieving the need to describe return values), the description covers preconditions, constraints, and cost. It is sufficiently complete for the tool's complexity, though it could mention job state requirements.

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?

Schema coverage is 100% (baseline 3). The description adds meaning by stating the new_limit must be greater than the previous limit, which is a semantic constraint not fully captured in the schema's 'must exceed' wording.

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 clearly states the tool's purpose: 'Expand a job by processing more records beyond the initial limit.' It uses a specific verb ('expand') and resource ('job'), and distinguishes from siblings by specifying it only applies to jobs originally submitted with a limit.

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

Usage Guidelines5/5

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

Explicit usage guidance: 'Only use this when the user wants MORE data processed.' It also provides when-not-to-use: 'If a job was submitted without limit, there is nothing to continue.' Additionally, it clarifies the new_limit constraint ('must be greater than the previous limit').

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.