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CatchAll (by NewsCatcher)

by com.newscatcherapi.catchall-mcp
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Initialize Query

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and fully meets it: it discloses that the endpoint is preview-only, does not create a job or start processing, produces non-deterministic LLM-generated suggestions, and does not persist them (they must be passed explicitly to `submit_query`). This is exactly the behavioral context an agent needs.

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 one-sentence summary is front-loaded, and the Use when / Do not use when / Key behavior sections are tight bullet lists. No sentence is filler; the layout lets an agent scan intent, exclusions, and behavioral caveats quickly.

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?

The description is complete for a preview tool: it covers selection intent, the key alternative, side-effect profile, non-determinism caveat, and reuse path. An output schema exists, so not restating return values is acceptable, and no required behavioral or usage context appears missing.

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?

Input schema coverage is 100%, so the schema already documents all four parameters and their detailed constraints. The description does not add parameter-level meaning beyond mentioning `date_modification_message`, which appears to be an output rather than an input; 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?

Opens with a specific verb and object: 'Preview suggested validators, enrichments, and date ranges before submitting.' It reinforces the scope with 'preview-only endpoint' and explicitly distinguishes itself from the processing-oriented sibling `submit_query`, so an agent can tell them apart by intent.

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?

Provides explicit 'Use when' conditions for inspecting/editing suggestions and previewing date adjustments, plus a 'Do not use when' condition that routes to `submit_query` for immediate processing. This gives an agent clear decision criteria rather than leaving the choice to inference.

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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