faf_scoreScore .faf YAML content with the always-33 faf-kernel (WASM). Returns 0-100 (capped). The same score faf-cli and every FAF MCP give. Runs at the edge.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Raw .faf YAML content. Souls with a [faf] section have it extracted automatically. |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "properties": {
- "content": {
- "items": {
- "properties": {
- "text": {
- "description": "Score line, e.g. \"FAF SCORE: 85/100 (85%) ◇ BRONZE\".",
- "type": "string"
- },
- "type": {
- "const": "text",
- "type": "string"
- }
- },
- "required": [
- "type",
- "text"
- ],
- "type": "object"
- },
- "type": "array"
- },
- "isError": {
- "type": "boolean"
- }
- },
- "required": [
- "content",
- "isError"
- ],
- "type": "object"
-}New value: +nullOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "content": {
+ "items": {
+ "properties": {
+ "text": {
+ "description": "Score line, e.g. \"FAF SCORE: 85/100 (85%) ◇ BRONZE\".",
+ "type": "string"
+ },
+ "type": {
+ "const": "text",
+ "type": "string"
+ }
+ },
+ "required": [
+ "type",
+ "text"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "isError": {
+ "type": "boolean"
+ }
+ },
+ "required": [
+ "content",
+ "isError"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (read-only, idempotent, closed-world, non-destructive), so the bar is lower. The description still adds real value: the score is deterministic via the 'always-33' kernel, capped at 0-100, and consistent with faf-cli and other FAF MCPs. It does not describe failure behavior on malformed YAML.
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?
Four short sentences, front-loaded with the verb-resource and the return contract. 'Runs at the edge' adds little, but overall it is tight and earns its space.
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 single-parameter, deterministic scoring function with no output schema, the description gives an adequate output range and provenance. What the number means and error handling are not covered, but nothing critical to invocation is missing.
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 100% and the schema param description even explains [faf]-section extraction, which the tool description does not repeat. Baseline 3 is appropriate since the schema carries the parameter burden entirely.
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 specific verb ('Score') and resource ('.faf YAML content'), and adds the output contract (0-100, capped) plus the 'always-33 faf-kernel (WASM)' engine. It is clearly distinguishable in function, but it never differentiates itself from close siblings like faf_validate or faf_analyze.
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?
There is no explicit when-to-use or when-not-to-use guidance. Given siblings such as faf_validate, faf_gate, and faf_get_tier, the description should route the agent, but only implies usage through the returned score value.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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