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

Annotations already cover readOnly/idempotent/non-open-world safety, so the bar is lower. The description adds genuinely useful context beyond them: "Uses no AI and no credits," which matters for cost-sensitive agent planning. The credits/AI-usage mention also signals the payload contains billing-relevant data.

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?

Front-loaded with the payoff first; the long enumeration is dense but each item earns its place by describing the payload. The trailing "Read it first" and "Uses no AI and no credits" sentences are short and high-value.

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?

An output schema exists, so return-value detail is not the description's burden, and annotations cover the safety profile. Combined with the usage cue and cost disclosure, the definition is essentially complete for a single-parameter aggregate reader.

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?

Schema coverage is 100% with a single documented project_id (UUID). The description adds no syntax or format detail beyond the schema, so the baseline of 3 for a fully-documented parameter set is appropriate.

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 precise aggregate read: counts of stakeholders, stories, requirements (by category and lifecycle status), subsystems, open questions, gaps, one suggested next step, and credits used. This composite nature clearly distinguishes it from siblings like projects_get, projects_assess, or projects_analyze. An agent can tell what it returns without opening the schema.

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?

"Read it first when starting work on a project" gives explicit when-to-use guidance, which is valuable given the many sibling read tools. It does not, however, name a when-not condition or a specific alternative for narrower queries.

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