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iranti_ingest

Process raw text content to extract and store atomic facts in persistent memory for AI coding agents, enabling shared knowledge across development tools.

Instructions

Ingest a raw text block and let the Librarian chunk it into atomic facts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesEntity in entityType/entityId format.
contentYesRaw text content to ingest.
confidenceNoRaw confidence score.
sourceNoSource label for provenance.
agentNoOverride the default agent id.
agentIdNoAlias for agent. Override the default agent id.
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the Librarian chunks text into atomic facts, which hints at processing behavior, but lacks critical details: it doesn't specify whether this is a read-only or mutating operation, what happens to the ingested data (e.g., storage, indexing), authentication needs, rate limits, or error handling. For a tool with no annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and outcome, making it easy to parse. Every part of the sentence contributes to understanding the tool's function, with zero waste.

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 complexity (6 parameters, no annotations, no output schema), the description is insufficiently complete. It lacks details on behavioral traits, output format (what 'atomic facts' look like), error conditions, and usage context relative to siblings. Without annotations or an output schema, the description should provide more context to guide the agent effectively, but it falls short.

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%, meaning all parameters are documented in the input schema. The description does not add any semantic details beyond what the schema provides (e.g., it doesn't explain the 'entity' format further or clarify the relationship between 'agent' and 'agentId'). According to the rules, with high schema coverage, the baseline score is 3, as the description doesn't compensate with extra parameter insights.

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 clearly states the action ('ingest a raw text block') and the outcome ('chunk it into atomic facts'), specifying both the verb and resource. It distinguishes this as an ingestion/chunking operation, which is different from siblings like query, search, or write tools. However, it doesn't explicitly contrast with specific siblings like 'iranti_write' or 'iranti_observe' to fully differentiate usage contexts.

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 provides no guidance on when to use this tool versus alternatives. With multiple sibling tools available (e.g., iranti_write, iranti_query, iranti_search), there is no indication of prerequisites, typical use cases, or exclusions. This leaves the agent without context for selecting this tool over others in the same server.

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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