Counts a project's layers and recommends creating a wiki when sessions, learnings, or recalls reach threshold levels.
Layer counts + wiki-earn suggestion for a project. The suggestion rule: (≥3 sessions AND ≥5 learnings) OR (≥3 architecture-phrased recalls) on a project with no wiki → suggest generating the wiki.
| Name | Required | Description | Default |
|---|---|---|---|
| project | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing side effects and behavior. It reveals that the tool may suggest wiki generation based on session/learning/recall thresholds, which is useful, but it does not state whether the tool is read-only, makes changes, or how it behaves with an unspecified or empty project. No contradiction with annotations exists because there are none.
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 compact and front-loads the core deliverable before diving into the suggestion rule. The second sentence is a dense but well-structured rule. However, the use of undefined terms like 'layer counts' and 'wiki-earn' means brevity comes at the cost of clarity.
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?
Although an output schema exists, the description omits essential context such as what 'layer counts' refers to, when this tool is the right choice, and what 'wiki-earn' means. The suggestion rule is the only concrete contextual detail. For a one-parameter tool this is passable but not complete.
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 description coverage is 0%, so the description must explain the 'project' parameter itself. It mentions 'for a project' and 'on a project with no wiki,' which clarifies that the parameter selects a project, but it does not explain the default '', what format is expected, or what happens with an empty value. Coverage is inadequate given zero schema descriptions.
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 says the tool provides layer counts and a wiki-earn suggestion, and it gives the precise rule for when to suggest wiki generation. However, 'layer counts' and 'wiki-earn' are undefined jargon, and there is no explicit verb like 'returns' or 'computes,' so the purpose is only moderately clear. It does differentiate from sibling ingest/recall tools by being a stats/analysis 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?
No guidance is given about when to call this tool versus siblings like recall, learn, or retire. The suggestion rule hints at a monitoring/health-check use case, but that is left to inference. There are no explicit alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.