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create_entity

Create a single entity (a company or person).

name is required plus at least one identifying field: either description or additional_attributes.company_attributes.domain.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesEntity name (required).
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
descriptionNoOptional description of the entity.
entity_typeNoOptional entity type: 'company' (default) or 'person'.
external_entity_idNoOptional customer-supplied identifier linking this entity to an external system's record (new in 1.6.3).
additional_attributesNoOptional structured attributes. For companies, use `{"company_attributes": {"alternative_names": [...], "domain": "...", "key_persons": [...], "description": "..."}}`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It mentions the constraint that at least one identifying field is required, but omits side effects, error handling, idempotency, or return value behavior. The description is minimal for a creation tool.

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?

Two concise sentences with no redundant information. Every part is meaningful and front-loads the core action and constraint.

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

Completeness3/5

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

Given the 6 parameters and existence of an output schema, the description is adequate but incomplete. It lacks information about duplicate handling, error scenarios, and usage context. The schema covers parameter details, but overall context is thin.

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?

The description adds value beyond the schema by clarifying the business rule that 'name' is required and at least one of 'description' or 'additional_attributes.company_attributes.domain' must be provided. This constraint is not explicitly in the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates a single entity, either a company or person, and specifies required and optional fields. It distinguishes from the sibling 'create_entities_batch' by implication, but does not explicitly differentiate.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like 'create_entities_batch' or 'update_entity'. There is no mention of prerequisites or situational use.

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.