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dakera_extract

Extract entities, topics, key phrases, and summaries from text using configurable AI providers with automatic fallback.

Instructions

Extract structured information (entities, topics, key phrases, summary) from arbitrary text using the configured provider hierarchy: per-request override → namespace default → server default → GLiNER local. Supported providers: gliner (zero-config local ONNX), openai, anthropic, openrouter, ollama, none.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to extract information from
namespaceNoNamespace whose default extractor config is used. If omitted, the server-level default is used.
entity_typesNoGLiNER entity type labels (e.g. ["person", "org", "location"]). Only used when provider is `gliner`.
extractor_overrideNoPer-request provider override — highest priority in the resolution hierarchy. Fields: provider, model, base_url, api_key.
Behavior3/5

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

The description discloses the provider resolution order and notes that API keys are never persisted. However, it does not explicitly state that the tool is read-only or mention any side effects, rate limits, or authorization requirements, which would be valuable given the absence of annotations.

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?

The description is concise but informative, front-loading the main purpose. It could be slightly more concise, but every sentence adds value (purpose, provider details). The structure flows logically from action to configuration.

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

Completeness4/5

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

Given the tool's complexity (4 parameters, nested objects), the description is fairly complete. It explains the provider resolution and use of parameters. The absence of an output schema is acceptable as descriptions need not explain return values. Minor gap: no mention of typical response structure or behavior when provider is 'none'.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining the provider hierarchy and that extractor_override has highest priority, which is not in the schema. It also clarifies that entity_types are only for gliner, which is useful context beyond the schema.

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 clearly states the tool extracts structured information (entities, topics, key phrases, summary) from arbitrary text, which is a specific verb+resource combination. It distinguishes itself from sibling tools (e.g., search, recall) by focusing on extraction rather than retrieval or storage.

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 provides context on when to use it (for extracting structured info) and explains the provider hierarchy, which guides selection. However, it does not explicitly state when not to use it or list alternative tools for different tasks.

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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