generate_faf_from_githubGenerate a .faf file from any public GitHub repository WITHOUT cloning. Extracts 6 Ws from README, analyzes stack from languages and package.json, and generates Championship-grade AI context. Returns .faf content, quality score, and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | GitHub repository URL or owner/repo format (e.g., "facebook/react" or "https://github.com/facebook/react") |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "properties": {
- "content": {
- "items": {
- "properties": {
- "text": {
- "description": "Human-readable tool result.",
- "type": "string"
- },
- "type": {
- "const": "text",
- "type": "string"
- }
- },
- "required": [
- "type",
- "text"
- ],
- "type": "object"
- },
- "type": "array"
- },
- "isError": {
- "description": "True when the tool failed.",
- "type": "boolean"
- }
- },
- "required": [
- "content",
- "isError"
- ],
- "type": "object"
-}New value: +nullOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "content": {
+ "items": {
+ "properties": {
+ "text": {
+ "description": "Human-readable tool result.",
+ "type": "string"
+ },
+ "type": {
+ "const": "text",
+ "type": "string"
+ }
+ },
+ "required": [
+ "type",
+ "text"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "isError": {
+ "description": "True when the tool failed.",
+ "type": "boolean"
+ }
+ },
+ "required": [
+ "content",
+ "isError"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and false destructiveHint. The description adds meaningful behavioral context beyond those hints: it does not clone, it extracts data from README, languages, and package.json, it generates a Championship-grade AI context, and it returns content plus quality score and metadata. No contradiction with the annotation hints.
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 at three sentences and front-loaded with the primary action. The only mild weakness is 'Championship-grade AI context', a promotional phrase that adds no precise behavioral meaning, but the overall density remains high and it does cover the key data points.
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 tool with a single parameter, no output schema, and relevant annotations, the description covers the prerequisites, the input form, the processing behavior, and the return fields (.faf content, quality score, metadata). It does not discuss error cases or network dependencies, but those are nonessential given the scope.
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 schema already describes the repo parameter completely with format examples. The description supplements this by clarifying that the repository must be public—important operational constraints that would not be obvious from the schema alone. That lifts it above the baseline for 100% schema coverage.
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 states a specific verb ('Generate'), a specific resource ('.faf file from any public GitHub repository'), and a distinguishing constraint ('WITHOUT cloning'). It tells the agent exactly what the tool produces, making it easy to separate from analysis, scoring, and validation sibling tools.
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 the main scenario: create a .faf file from a public GitHub repo without cloning. However, it does not explicitly say when to choose this tool over siblings like faf_analyze, faf_validate, or generate_faf_from_github, nor does it provide exclusions or alternative routing.
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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