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

With no annotations provided, the description carries the burden and handles the most important behavioral trait well: the read-only, auth-optional model for public spaces versus login+membership for private ones. It omits failure modes (invalid slug) and payload-size/pagination behavior, which are relevant for a tool that reads an entire graph.

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?

Three short sentences, front-loaded with the action and its output format, followed by the access rules. Every sentence earns its place with no repetition.

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?

No output schema exists, but the description still names the return format (RDF/JSON-LD), so an agent knows what to expect. Combined with the auth model, this is nearly complete for a simple one-parameter read tool; only slug provenance and error behavior are missing.

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 single required parameter 'slug' has 0% schema description coverage, and the description only implies its meaning via 'a space's graphs' – it never says the slug identifies the space or where to obtain it (e.g., list_spaces). It adds minimal meaning beyond the schema.

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?

States a specific verb and resource: 'Read all RDF (JSON-LD) in a space's graphs.' It is clear this returns raw graph content rather than a search or query result, though it never explicitly names the sibling tools (sparql, search, list_spaces) it could be confused with.

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?

Gives concrete when-to-use context: public spaces are readable without login, private spaces require login and membership, and it explicitly warns against prompting for login on public spaces. It does not, however, point to alternatives for content that could be retrieved more cheaply (search, sparql).

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