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

With no annotations, the description carries the full behavioral burden and does so well: it discloses the exact return shape, the shared-row detail between TikTok and Meta, that it mirrors the dashboard view, and the silent-zero-rows failure mode. The word 'List' and the tool name also make the read-only nature clear.

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?

The description is front-loaded with purpose, followed by behavioral nuance, usage sequencing, and a required/example block. Every sentence adds value, and there is no filler or repetition of the tool name.

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?

For a zero-parameter, no-output-schema tool, the description is complete: it explains what is returned, the cross-provider sharing behavior, when to call it relative to siblings, and the main failure mode. An agent has everything it needs to select and invoke this tool correctly.

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 tool has zero parameters, so there is little for the description to add beyond the schema. The description reinforces this with 'REQUIRED: nothing' and a concrete example call, mmp_get_state({}), which is useful for confirming no arguments are needed.

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 and resource: 'List MMP connections, synced app metadata, and cohort configs for the current user.' It also disambiguates itself by noting it returns cohort configs rather than cohort data and explicitly calls itself a mirror of Meta's mmp_get_state, making it distinct from siblings like mmp_fetch_cohorts.

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 explicitly says to call this BEFORE mmp_save_cohort_config or mmp_fetch_cohorts to discover valid connection_id, app_ref, and KPI event names. It also warns that typing an unsupported KPI silently returns zero rows, giving the agent concrete guidance on when and why to use this tool.

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