servicehawk-ad-intel
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| AD_INTEL_DATA_DIR | No | Override the location of the data directory. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| build_meta_ad_library_urlA | Create a public Meta Ads Library search URL for manual competitive ad research. |
| capture_public_ad_pageA | Fetch and store metadata/text from an allowlisted public ad-library page without login or bypass behavior. |
| record_ad_swipeA | Record one observed ad pattern into the local swipe file and score its reusable winning signals. |
| score_ad_patternA | Score a copied ad pattern for reusable winning signals without saving it. |
| list_ad_swipesB | List stored ad swipe observations filtered by vertical, offer, and minimum score. |
| summarize_winning_patternsC | Summarize top stored swipe patterns and recommend ServiceHawk remix angles. |
| generate_servicehawk_variantsB | Generate ServiceHawk-safe Meta still-image ad copy variants from known winning angle families. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool targets a distinct step in the ad research workflow: generating variants, building search URLs, capturing pages, scoring/recording patterns, listing, and summarizing. The only mild overlap is between generate_servicehawk_variants and summarize_winning_patterns, but their inputs and outputs are clearly different enough.
All tool names follow a predictable snake_case verb_noun pattern: generate_, build_, capture_, record_, score_, list_, summarize_. This makes the tool set easy to navigate and mentally model.
Seven tools is well-scoped for an ad intelligence server, covering generation, capture, evaluation, storage, retrieval, and synthesis without unnecessary duplication. Each tool earns its place in the workflow.
The core ad-research lifecycle is covered: capture, score, record, list, and summarize. The main gap is the lack of update/delete operations for stored swipes, but agents can still complete the primary workflow without them.