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record_ingest

Ingest canonical Customer Graph records with required per-field provenance and Data Rights Ledger blocks; tag customer-authored text as untrusted and quarantine instruction-shaped content at ingest.

Instructions

Ingest a canonical Customer Graph record (person / company / deal / ticket / conversation / activity). Every field must carry provenance; a Data Rights Ledger block is required. Customer-authored conversation text is tagged untrusted and instruction-shaped content is quarantined at ingest.

Input Schema

NameRequiredDescriptionDefault
kindYes
fieldsYes
record_idNo
data_rightsNo
external_idsNo
field_provenanceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose important ingest safeguards: required provenance, required ledger, untrusted tagging, and quarantine of instruction-shaped content. However, it omits core mutation behavior such as whether records are created/updated, idempotency, permissions, failure modes, and response behavior.

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?

Three dense sentences, front-loaded with the action and followed by the non-obvious constraints; there is no filler. The final security sentence adds specific, useful information rather than restating the name.

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?

For a six-parameter nested ingest tool with no annotations and no output schema, the description covers governance requirements but not enough operational detail. An agent still lacks parameter-level guidance, return behavior, and error/idempotency expectations.

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 0% for six parameters, and the description only partially compensates by implying kind values and the need for field_provenance/data_rights. It leaves fields, record_id, external_ids, and nested object structures unexplained.

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 states a specific verb ('Ingest') and resource ('canonical Customer Graph record') and enumerates record kinds, so an agent knows the action and target. It does not explicitly name or distinguish siblings such as record_read/record_search, but the ingest verb makes the distinction largely implicit.

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

Usage Guidelines3/5

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

It gives implied usage and prerequisites: records must carry provenance and include a Data Rights Ledger block. It does not state when to choose this over sibling tools or when not to use it, nor does it explain update-vs-create conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.