Entity Enricher

Import semantic concepts

import_semantic_concepts

Resolve 1..1000 texts against one concept type. mint=false returns exact/matched/would_mint outcomes without minting; mint=true creates misses and requires owner (reporting requires editor). Resolution can call embeddings and the identity judge even in report mode. Review would_mint rows before authorizing creation. A new type may be initialized with embedding_model; existing types cannot switch spaces through import. Returns per-text outcomes. See enricher://docs/semantic-ids.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "texts": "<texts>",
      "concept_type": "<concept_type>"
    },
    "name": "import_semantic_concepts"
  }
}

Input Schema

Name	Required	Description
`mint`	No	False = report only; True = create the unmatched rows (owner role).
`texts`	Yes	Identity texts to resolve (1..1000).
`judge_floor`	No	Similarity at or above which a candidate is put to the identity judge. Omit to use the organization default (Settings → Organization).
`concept_type`	Yes	Concept type (slice) to resolve against.
`embedding_model`	No	Composite key (provider::model) seeding a NEW concept type's slice — an import may open the type it resolves into. Refused when that type's vocabulary already lives in another model.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 25, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond annotations, the description discloses key side effects and runtime behavior: minting creates misses, resolution can invoke embeddings and the identity judge even in report mode, and embedding_model can initialize a new type. This adds material context about permissions, side effects, and model coupling that annotations alone would not convey.

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?

The description is dense but every sentence earns its place: scope, modes, permissions, side-effect warnings, embedding restrictions, and return summary are all covered in six sentences. It is front-loaded with the core action and mode distinction.

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

Completeness5/5

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

For a tool with an output schema, the description need not explain return shapes; it already says 'returns per-text outcomes.' Combined with permission details, mode distinctions, embedding caveats, and a docs link, an agent has what it needs to select and invoke the tool correctly.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful semantics: it explains the mint flag's outcome modes, describes embedding_model as a composite key used only for new types, and clarifies that a type's vocabulary cannot switch embedding spaces. This goes beyond the schema's field-level descriptions.

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 opens with a specific verb and resource: 'Resolve 1..1000 texts against one concept type.' It clearly distinguishes this bulk-resolution/minting tool from sibling tools like add_semantic_concept by emphasizing range, concept-type scoping, and optional minting behavior.

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?

The description gives explicit mode guidance: mint=false is report-only, mint=true creates misses and requires owner, reporting requires editor. It also states a clear exclusion ('existing types cannot switch spaces through import') and advises reviewing would_mint rows before authorizing creation. It does not name alternative tools directly, so it stops short of a 5.

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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