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. Dates show when Glama detected each change.
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?
'WITHOUT cloning' is a meaningful behavioral disclosure that goes beyond the schema or annotations. 'Returns .faf content, quality score, and metadata' also provides an high-level view of side effects/results. Since readOnlyHint and idempotentHint already signal safety, the description adds extra value by clarifying the remote no-cloning behavior and the public-only constraint without contradiction.
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: three sentences cover the core action, the generation approach, and the key output items. The only 'deficit' is the phrase 'Championship-grade AI context', which is promotional rather than informative, and the slightly cryptic '6 Ws' that are not defined. Still, the definition is well front-loaded and mostly every sentence adds functional value.
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 a single documented parameter, solid annotations, and no output schema, the description covers the necessary ground: input source, key constraint, processing summary, and return values. It would be richer if it described the structure of the metadata or the semantics of the quality score, but it gives enough context for an agent to select and call the tool reasonably.
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 fully documents the single `repo` parameter including accepted formats, so schema_description_coverage is 100%. The description adds a publicness constraint and mentions README/package.json as analysis sources, but it does not materially enrich the semantics of the parameter beyond that; the 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 names a specific action ('Generate a .faf file'), a clear resource ('from any public GitHub repository'), and a defining condition ('WITHOUT cloning'). It effectively sets this tool apart from the sibling faf_* tools, which target analysis, scoring, or local operations rather than repository-to-faf generation.
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 clearly implies when to use it — whenever a .faf file is needed for a public GitHub repo without cloning — but it does not explicitly ruleed out alternatives or mention when another sibling tool should be preferred. There are no exclusionary conditions, so the guidance remains implied rather than fully stated.
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.