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

No annotations are provided, so the description bears the transparency burden. It discloses that read access depends on space visibility (public vs private), which is valuable behavioral context. It does not mention potential large payloads for spaces with many graphs, nor confirm absolutely no side effects beyond the 'read' verb.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences convey purpose and access requirements without redundancy. Every word earns its place; the description is front-loaded with the primary action.

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

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

Given the tool's simplicity (one parameter, simple read operation), the description covers the essential purpose and access constraints. Minor gaps remain in specifying the exact response envelope (e.g., raw JSON-LD vs wrapped) and error conditions when the slug is invalid or access is denied.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one required parameter, 'slug', with no description (0% coverage). The description mentions 'a space's graphs' but never explains that 'slug' is the space identifier or the expected value format, leaving the agent to infer the semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool reads all RDF (JSON-LD) in a space's graphs, specifying both the verb (read) and the resource (all RDF in space graphs). This distinguishes it from sibling tools like brainkb_sparql (query) and brainkb_search (search).

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

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

The description notes public spaces are readable by anyone while private spaces require membership, providing access context. However, it does not explicitly contrast this tool with alternatives like brainkb_sparql for querying or brainkb_delta for examining changes, leaving the usage boundaries implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.