TABStack

TABStack

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78/ 100
B
Score Breakdown78%

Lost points on 4 checks

  • time3/15
  • efficiency2/10
  • zero errors0/5
  • Context70/14
Eval Score (70% weight)75%
cost$0.03 (<$0.05, excellent)+10
time140s (90s–150s, slow)+3
efficiency19 tool calls (≤20, acceptable)+2
found docsTouched docs at docs.tabstack.ai: yes (8 matching tool calls)+10
zero errorsTool outputs with errors/tracebacks: 1+0
syntax validCode compiles/parses correctly+5
created filesGenerated files: 2 (need ≥1)+10
full executionMethod: full_execution, Exit: 0+35
Checklist Score (30% weight)86%
Context7
+0
llms.txt
Link+14
MCP Server
Link+14
Typed SDK
Link+14
OpenAPI
Link+14
Agent Skills
Link+14
CLI
Link+14

Checklist determined by AI. If something looks wrong, .

Final Score
A: >=90%B: 75-89%C: 55-74%D: <55%
(75 × 0.7) + (86 × 0.3)= 78%
Replay
Using https://docs.tabstack.ai/api, complete the following task:

Using TABStack, build me a script that runs a small web research workflow:

1. Run TWO related searches:
   a. A broad query: "grid-scale battery storage advances"
   b. A more specific, recency-focused query: "grid-scale battery storage breakthroughs 2025"
2. Merge the results from both searches, deduplicate by URL, and select the top 5 most relevant results
3. If TABStack supports fetching/extracting page content, retrieve the full content of the single most relevant result and write a 2-3 sentence summary grounded in that fetched content (not just the snippet)
4. Print the output as JSON with two keys:
   - "results": the top 5 deduplicated results, each with title, url, and snippet (or description)
   - "summary": your grounded 2-3 sentence summary of the top result (include the source url)
opencode.json

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Run completed Oct 23, 2026, 4:34 PM