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antientropy_read

Read full article content with line numbers from the AntiEntropy Resource Portal using article slugs. Access nonprofit governance, compliance, and HR policy articles.

Instructions

Read an article's content by slug.

Returns content with line numbers (cat -n format). Use antientropy_glob or antientropy_grep to discover slugs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
article_slugYes
offsetNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns content with line numbers (cat -n format), which adds useful context beyond basic reading. However, it doesn't cover other aspects like error handling, permissions, or rate limits, leaving gaps for a tool with no annotation coverage.

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 appropriately sized and front-loaded, with three concise sentences that each serve a clear purpose: stating the action, specifying the return format, and providing usage guidance. There is zero waste, making it highly efficient.

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 complexity (a read operation with parameters), no annotations, and an output schema present, the description is reasonably complete. It explains the return format and usage context, but since the output schema exists, it doesn't need to detail return values. However, with 0% schema coverage and no annotations, it could benefit from more parameter guidance.

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 0%, so the description must compensate. It mentions 'article_slug' and implies usage with offset and limit through 'Returns content with line numbers,' but doesn't explicitly explain the parameters' roles or semantics. This adds minimal value beyond the schema, resulting in a baseline score due to incomplete compensation.

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 verb ('Read') and resource ('article's content by slug'), making the purpose specific and understandable. It distinguishes from siblings by mentioning antientropy_glob and antientropy_grep as discovery tools, but doesn't explicitly differentiate from antientropy_categories, which might be a related but distinct operation.

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 clear context by stating 'Use antientropy_glob or antientropy_grep to discover slugs,' which guides when to use this tool (after slug discovery) and mentions alternatives for that purpose. However, it lacks explicit exclusions or comparisons to all siblings like antientropy_categories, leaving some ambiguity.

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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