Engagemii Citation Watch

Pull licensed company records

data_records

Pull full licensed records from the Engagemii dataset (all columns, fingerprinted, the same shape a purchased file delivers). Metered: every record returned uses one unit of the account's purchased API quota. Returns 25 records by default and at most 100 per call. Larger pulls are available through the REST API at https://api.engagemii.com/api/v1/data/records with the same key. Requires an Engagemii data API key.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	records to draw (each costs 1 quota). Default 25, max 100 in-chat.
`scope`	No
`states`	No
`api_key`	No	Your Engagemii data API key (edk_...). Only needed when the key is not already sent as a request header.
`scoreMax`	No
`scoreMin`	No
`industries`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 28, 2026

addedInput schema / properties / api_key

Added value: +{
+  "description": "Your Engagemii data API key (edk_...). Only needed when the key is not already sent as a request header.",
+  "type": "string"
+}

First observedSep 23, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations declare readOnlyHint=false, openWorldHint=true, idempotentHint=false and destructiveHint=false; the description adds real value beyond them by disclosing the metered quota consumption (one unit per record) and the API-key requirement. It does not contradict the annotations — quota consumption plausibly explains the non-read-only hint.

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?

Four dense sentences with the core capability and cost model front-loaded, then limits, then the REST fallback and auth requirement. Each sentence carries weight; no padding.

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?

For a 7-parameter, no-output-schema tool it covers quota, limits, auth and a scale-out path well, but neglects the semantics of five filter parameters, which is the main thing an agent needs to shape a useful call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 29% (just limit and api_key are documented), leaving scope, states, scoreMax, scoreMin and industries unexplained in structured data. The description restates the limit/quota relationship but adds nothing for the five undocumented filters, so it fails to compensate for the coverage gap.

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?

States a specific verb and resource ('Pull full licensed records from the Engagemii dataset'), specifies the column shape ('all columns, fingerprinted, the same shape a purchased file delivers'), and is distinguishable from siblings like dataset_sample and search_companies. An agent knows exactly what this returns without consulting another tool.

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

Usage Guidelines4/5

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

Explains the quota cost per record, the default (25) and in-chat ceiling (100), and routes larger pulls to the REST endpoint. It does not explicitly contrast with sibling tools such as search_companies or dataset_sample, so there is no when-not guidance, but the operational context is clear.

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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