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absorb

Retrieve complete knowledge modules by exact name to access full documentation, code examples, and best practices for specific topics.

Instructions

Load the full content of a knowledge module by its exact name/slug. Returns the complete module text (typically 2,000-20,000 words) with code examples, best practices, and references. Use after forage to read a specific module in full. Behavior: looks up the module by slug, returns full markdown content. If not found, suggests using forage to search. Example: absorb("react-mastery") returns the complete React mastery guide.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesExact module slug (kebab-case). Get slugs from forage results. Examples: "react-mastery", "kubernetes-hpa-guide", "owasp-top-10-checklist"
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behaviors: it returns complete module text with details on content type and length, describes the lookup mechanism, specifies error handling (suggests using forage if not found), and provides a concrete example. It doesn't mention permissions or rate limits, but covers core operational behavior adequately.

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 efficiently structured with zero waste: first sentence states purpose, second adds context on output, third provides usage guidance, fourth describes behavior, and fifth gives an example. Every sentence earns its place and is front-loaded with essential information.

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?

For a single-parameter read operation with no annotations and no output schema, the description is quite complete: it explains purpose, usage context, behavior, error handling, and provides an example. The main gap is lack of explicit mention of whether this is a read-only operation (though implied by 'Load'), but overall it provides sufficient context for agent decision-making.

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%, providing full documentation for the single parameter. The description adds minimal value beyond the schema by mentioning 'exact name/slug' and referencing 'forage results,' but doesn't provide additional syntax or format details. Baseline 3 is appropriate given the schema does the heavy lifting.

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 specific action ('Load the full content') and resource ('knowledge module by its exact name/slug'), distinguishing it from siblings like 'forage' (search) and 'recall' (likely retrieval). It specifies the verb+resource combination precisely without being tautological.

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

Usage Guidelines5/5

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

Explicit guidance is provided: 'Use after forage to read a specific module in full' and 'If not found, suggests using forage to search.' This clearly indicates when to use this tool versus the 'forage' alternative, establishing a workflow dependency.

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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