Skills vs. MCP vs. prompts: which agent setup works best?
The article compares different agent setups for converting PDF pages into HTML. The step-by-step approach achieved the highest accuracy and pass rate among the tested methods. The findings highlight the importance of structured prompts and self-checking in improving performance.
- ▪The step-by-step setup achieved 95% accuracy and cleared all 10 pages with a structural fidelity score of 95%.
- ▪The baseline setup had an accuracy of 82% and cleared 9 out of 10 pages.
- ▪The study utilized the Agent Voyager Project to standardize and record the performance of various agent configurations.
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Story provenance
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Record
| Original publisher | AVP |
| Canonical URL | https://www.agentvoyagerproject.com/captains-log/1 |
| Publication time | Tue, 26 May 2026 14:29:08 +0000 |
| Retrieval time | 2026-05-26T14:37:49.954Z |
| Last seen | 2026-05-26T14:37:49.954Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 8yQG9DXgoVVR |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Skills vs. MCP vs. prompts: which agent setup works best?Welcome to the Captain's Log, where we break down the voyages our agents undertook each week. For this inaugural run, we set out to test how different agent setups (skills vs MCPs vs prompts) compare.The task: read a PDF page and rebuild it as a webpage (from ParseBench, a public LlamaIndex benchmark). Every setup uses the same model (claude-haiku-4-5), so any differences come from how the agent is set up, not the model itself.How we score it: each page gets a structural-fidelity score from 0 to 100% (column headers, row count, cell content, merged-cell topology, compared against the reference HTML).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at AVP.
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