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list_dataset_entities

List the entities contained in a dataset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination (default: 1).
searchNoOptional text filter on entity name.
statusNoOptional status filter: 'pending', 'enriching', 'ready', or 'failed'.
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
sort_byNoOptional sort field: 'created_at', 'name', or 'status'.
page_sizeNoNumber of results per page (default: 100).
dataset_idYesThe dataset ID whose entities you want.
sort_orderNoOptional sort direction: 'asc' or 'desc'.
entity_typeNoOptional type filter: 'company' or 'person'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are present, so the description must carry the full burden. It only says 'List', implying read-only, but does not explicitly state that the operation has no side effects, requires no special permissions, or behaves in any other notable way. Behavioral traits like pagination statefulness or rate limits are not disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no unnecessary words. However, it could be slightly restructured to front-load the core action while also hinting at pagination or filtering options.

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

Completeness2/5

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

Given the complex input schema (9 parameters, including pagination and multiple filters) and the presence of an output schema, the description is too minimal. It should at least mention that pagination, filtering, and sorting are supported to give a complete picture of the tool's capabilities.

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 description coverage is 100%, so the input schema already thoroughly documents each parameter. The description adds no additional semantic value 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?

The description clearly states the action ('list'), the resource ('entities'), and the scope ('contained in a dataset'). It differentiates from sibling tools like list_entities (presumably lists all entities) and get_entity (single entity).

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?

The description gives no guidance on when to use this tool vs alternatives. Siblings such as list_entities or list_datasets exist but are not mentioned, and no context about prerequisites or use cases is provided.

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.