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Glama

CatchAll

by com.newscatcherapi

list_entities

List your entities.

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, max: 1000).
project_idNoOptional filter to entities belonging to a specific project.
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. Changed1 schema field changed
    • addedInput schema / properties / project_id
      Added value: +{
+  "default": "",
+  "description": "Optional filter to entities belonging to a specific project.",
+  "type": "string"
+}
  2. First observed

TDQS

C2.2/5.0
Behavior1/5

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

With no annotations provided, the description must communicate behavioral traits. It only says 'List your entities,' which gives no information about read-only safety, pagination limits, filtering behavior, or response format. The description adds no value beyond the tool's name, and the schema carries all behavioral context.

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

Conciseness2/5

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

The description is extremely concise in length, but it is under-specified rather than appropriately sized. It consists of a single short sentence that does not convey enough information about the tool's purpose or behavior. While it is front-loaded, the lack of substance makes it poorly structured for agent consumption.

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?

Despite having an output schema and complete parameter documentation, the description misses critical context: what entities are, when to use this tool versus list_dataset_entities, and any behavioral constraints. For a tool with 9 optional parameters, this is insufficient to guide correct invocation. The agent must rely entirely on the schema, which does not provide high-level usage context.

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 all nine parameters are documented. The description adds no parameter-specific detail beyond what the schema provides, which is the baseline expectation. Since the schema already defines each parameter's purpose and default, the description does not need to repeat them.

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

Purpose3/5

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

The description states a verb ('list') and a resource ('entities'), which is clear at a basic level. However, 'entities' is ambiguous without context (the schema shows entity_type but the description doesn't clarify that entities are companies/persons). It does not distinguish from sibling tools like list_dataset_entities or list_projects, so an agent cannot easily infer which list tool is appropriate.

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 on when to use this tool vs. alternatives. There is no mention of scope (e.g., 'all your entities across projects') or conditions that would make a sibling tool more appropriate (e.g., list_dataset_entities). The agent must infer usage from the schema alone, which is insufficient.

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