llmconveyors-mcp

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

With no annotations provided, the description carries the full disclosure burden and successfully covers critical safety ('Read-only, no side effects') and authorization ('Requires scope: sessions:read') traits. It lacks only operational details like pagination behavior or rate limits, which would be relevant for a 'list all' operation.

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?

Every sentence earns its place: sentence 1 defines the action and return payload, sentence 2 provides usage timing, sentence 3-4 disclose safety/auth, and sentences 5-6 map sibling alternatives. No redundancy with the schema (which is empty) and no filler.

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 zero parameters, no annotations, and no output schema, the description adequately compensates by covering authentication requirements, side-effect profile, and sibling differentiations. Minor gap: could hint at pagination or total result set size given the 'list all' scope.

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?

Baseline score applies per rubric ('0 params = baseline 4'). With no parameters in the input schema, there are no semantics to clarify beyond the baseline, and the description correctly implies no filtering is available (list 'all').

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 opens with a specific verb ('List') and clear resource ('saved source documents'), immediately clarifying the scope ('used as context for AI generation'). It effectively distinguishes from siblings by contrasting with 'content-get-source' (specific document retrieval) and positioning itself as the bulk listing operation.

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?

Explicitly states when to use ('before running agents') and provides clear alternative paths: 'Use content-get-source to read a specific document' and 'Use content-save to add or update documents.' This creates a decision tree for the agent, covering the read-specific and write alternatives.

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