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CatchAll

by com.newscatcherapi

initialize_query

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

TDQS

A4.9/5.0
Behavior5/5

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

Discloses preview-only nature (no job creation), non-deterministic LLM-generated suggestions, and how to reuse results. Fully transparent with no annotations needed.

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?

Five sentences structured into purpose, usage, and key behavior sections. No wasted words; every sentence adds value.

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?

Covers purpose, usage, behavioral traits, and parameter context. Output schema exists, so return values are documented elsewhere. Complete for a tool of this complexity.

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% with detailed parameter descriptions. Description adds value by providing usage context (e.g., guidance for `context` parameter about not asking for entity-relevance validators). Slightly above baseline.

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?

Uses specific verb 'preview' and resources 'validators, enrichments, and date ranges'. Clearly distinguishes from sibling tools like `submit_query` by stating it's a preview-only step.

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 use-when and do-not-use-when sections. Contrasts with `submit_query` for when to start processing. Provides clear conditions for using the tool.

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.

Resources