faf_scoreScore .faf YAML content via the Mk4 Zig-WASM engine. Returns 0-100 (capped). Same engine as xai-faf-rust + xai-faf-zig (parity-tested). Sub-ms 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. Dates show when Glama detected each change.
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?
The annotations already include readOnlyHint, idempotentHint, and destructiveHint. The description adds useful context beyond these: a specific Mk4 engine, a capped 0-100 output, parity with related engines, and a performance characteristic. No contradiction with annotations.
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 short, well-structured, and front-loaded. It communicates purpose, engine, output range, parity, and performance in three sentences with no wasted words.
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 one-parameter tool with complete schema coverage and a description that explains the returnable range, the definition is largely sufficient. There is no output schema, but the return format is stated clearly, so an agent has enough information to invoke the tool and interpret the result.
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 input schema covers the single content parameter fully, including details about [faf] extraction. The tool description does not need to add parameter detail. With 100% schema description coverage, a baseline of 3 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?
The description clearly states that the tool scores .faf YAML content, uses a specific engine, and returns a 0-100 capped score. This is a specific verb+resource combination. It does not explicitly distinguish itself from sibling tools like faf_validate or faf_get_tier, but its core purpose is unmistakable.
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 says what the tool does but gives no guidance on when to choose it over the many sibling tools listed, such as faf_validate or faf_estimate_tokens. The parity-testing note is useful technical context, but it does not route the agent toward or away from alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several faf_* tools overlap: faf_score, faf_validate, faf_analyze, and faf_gate all grade or gate content, while faf_get_tier and gf_analyze both address tier determination. Search and discovery also fragment into faf_collections_search, search_context, search_bfy_tag, list_tags, and tag_intel, making it easy for an agent to pick a nearly-equivalent tool.
The set consistently uses snake_case and leans heavily on the faf_ domain prefix, but the style is not uniform: faf_score, faf_validate, and faf_estimate_tokens are action-based, while faf_memory, faf_section, and faf_gate are noun- or verb-like with less clear command intent. Overall still readable, but the pattern is mixed.
19 tools is at the upper end of a reasonable number for a context/analysis system, especially with faf_ prefix family. However, some tools could be consolidated; faf_analyze overlaps faf_score+validate+get_tier, the several gone by explicit domain; several search/ag tools overlap, and the broad read-only surface leaves no obvious fns for create/update/delete operations.
This is a heavy read/analysis and scoring surface, but it lacks obvious write/update/delete primitives for persistent context entities. There are soul list/get and search tools, a faf generator, scoring/validation tools, and recommendations, but no create_soul, update_soul, delete_soul, or analogous persistent mutation operations for .faf/.fafm data. For 'persistent project context,' the set feels read-only and incomplete.