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Newscatcher

CatchAll (by NewsCatcher)

Official

initialize_query

Preview suggested validators, enrichments, and date ranges for a natural language query before submitting to adjust and refine parameters.

Instructions

Preview suggested validators, enrichments, and date ranges before submitting.

Use when:

  • You want to inspect/edit auto-generated validators/enrichments before submitting.

  • You want to preview date adjustments via date_modification_message.

Do not use when:

  • You want to start processing immediately with final inputs (use submit_query).

Key behavior:

  • Preview-only endpoint: does not create a job and does not start processing.

  • Suggestions are LLM-generated and not deterministic across calls.

  • To reuse suggestions, pass them explicitly to submit_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query to preview (required). If you plan to attach a company dataset via `connected_dataset_ids` in the subsequent `submit_query`, do NOT reference the company list here — entity filtering is applied automatically by the dataset, not by the query text.
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
contextNoOptional guidance on what to prioritize so suggested validators, enrichments, and dates align with your target data points. If a company dataset will be attached in `submit_query`, note that entity-relevance validators (e.g. `company_is_primary_subject`) will be auto-generated — do not ask for them here. Do not mention things like "company list will be attached". Focus on the event or topic only.
fetch_all_watchlist_newsNoWhen `True`, signals that the subsequent job will retrieve all news for connected watchlist entities without topic filtering. Pass this when you intend to use `fetch_all_watchlist_news=True` in `submit_query` so the previewed validators/enrichments are generated accordingly. Requires `connected_dataset_ids` to be set in `submit_query`. Default: `False`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Discloses key traits: preview-only, does not create a job, non-deterministic suggestions, reuse via submit_query. Covers important behavioral aspects beyond annotations (which are absent).

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?

Very concise, uses bullet points for clarity, front-loaded with the main purpose. Every sentence adds value.

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?

Adequately covers the preview workflow and key behaviors. Output schema exists, so return values need not be detailed. Could be slightly more explicit about what the preview returns, but overall 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% with detailed descriptions. The tool description adds workflow context but does not significantly enhance parameter-level meaning beyond the schema.

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?

Clearly states it previews suggested validators, enrichments, and date ranges before submitting. Distinguishes from sibling 'submit_query' by explicitly contrasting use cases.

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?

Explicitly lists when to use (inspect/edit suggestions before submitting) and when not (immediate processing, use submit_query). Provides clear alternative.

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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