Retrieve node-label and edge-type counts from a code knowledge graph. Use diagnostics=full to also list queryable properties.
Get node-label and edge-type counts. diagnostics=full also lists queryable properties.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| format | No | tree | |
| offset | No | ||
| project | Yes | ||
| diagnostics | No | none |
Changes observed during successful MCP inspections.
v0.11.0Input schema / properties / diagnosticsAdded value: +{
+ "default": "none",
+ "enum": [
+ "none",
+ "full"
+ ],
+ "type": "string"
+}Input schema / properties / formatAdded value: +{
+ "default": "tree",
+ "enum": [
+ "tree",
+ "json"
+ ],
+ "type": "string"
+}Input schema / properties / limitAdded value: +{
+ "default": 50,
+ "maximum": 500,
+ "minimum": 1,
+ "type": "integer"
+}Input schema / properties / offsetAdded value: +{
+ "default": 0,
+ "minimum": 0,
+ "type": "integer"
+}v0.10.4Output schema / (root)Previous value: -{
- "additionalProperties": true,
- "type": "object"
-}New value: +nullv0.9.0Output schema / (root)Previous value: -nullNew value: +{
+ "additionalProperties": true,
+ "type": "object"
+}v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only, idempotent, and non-destructive, so the description does not need to repeat safety traits. It adds the 'diagnostics=full' behavior for listing properties, which is beyond annotations. However, it doesn't detail output structure or limitations, but annotations cover a lot.
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 concise (two sentences) and front-loads the primary purpose. It mentions the diagnostics special case without excess detail, though it could be slightly more structured.
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 the tool's moderate complexity (5 params, enums) and lack of output schema, the description provides minimal but sufficient context for basic usage. It does not explain parameter interplay or output format, but annotations cover safety. Overall adequate but with gaps.
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 0% and the description mentions only the diagnostics parameter's effect, not explaining others like limit, format, offset, or project. With 5 parameters and no schema descriptions, the description fails to compensate, leaving agents to guess parameter purposes.
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 clearly states the tool retrieves node-label and edge-type counts, which is a specific resource and data type. It does not explicitly differentiate from siblings like query_graph or get_architecture, but the focus on schema/counts is distinctive.
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 implies usage for schema inspection but does not provide explicit guidance on when to use this over alternatives like query_graph or compare_graphs. It lacks context on typical use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.