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  "IssueTotal": 10,
  "Held": 10,
  "Issues": [
    {
      "Title": "Version inconsistency between pyproject.toml (0.1.2) and server.json (0.1.1)",
      "Excerpt": "## Summary\n\n`pyproject.toml:7` declares `version = \"0.1.2\"`. `server.json:6` declares `\"version\": \"0.1.1\"`. The two should match (or both derive from a single source of truth).\n\n## Reproduction\n\n```\n$ grep -A1 '^\\[project\\]' pyproject.toml | grep version\nversion = \"0.1.2\"\n\n$ grep '\"version\"' server.json\n  \"version\": \"0.1.1\",\n```\n\n## Why this matters\n\nDownstream consumers pinning a paperbanana version (e.g. `paperbanana>=0.1.2` in an integration that needs a recent fix) will see one number in pac",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/215",
      "PublishedAt": "2026-05-18T19:53:07.000Z",
      "State": "closed",
      "Comments": 1,
      "Reporter": "External",
      "Rank": "top",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Feature]: MCP: expose diagram run continuation (continue_run + optional user feedback)",
      "Excerpt": "### Problem or motivation\n\n## Motivation\nPaperBanana already supports a strong human-in-the-loop workflow from the CLI: paperbanana generate --continue / --continue-run <run_id> with optional --feedback, backed by load_resume_state and PaperBananaPipeline.continue_run in paperbanana/core/resume.py and paperbanana/core/pipeline.py. The README also documents this path and the Python API for continue_run.\n\nUsers driving the tool from Cursor / Claude Code via MCP only get generate_diagram, which alw",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/189",
      "PublishedAt": "2026-04-23T19:28:03.000Z",
      "State": "closed",
      "Comments": 0,
      "Reporter": "Contributor",
      "Rank": "top",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Feature]: MCP: add tools to continue a prior diagram or plot run (CLI / Studio parity)",
      "Excerpt": "### Problem or motivation\n\n## Problem\nAuthors often want to extend refinement after a first generation: more visualizer–critic rounds, or new rounds guided by short natural-language feedback, without redoing retrieval / planner / stylist from scratch.\n\nThe codebase already supports this for local workflows:\n\n- CLI: paperbanana generate --continue / --continue-run and related flags\n- Core: paperbanana.core.resume.load_resume_state and pipeline APIs that accept resume state\n- Studio: continue work",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/187",
      "PublishedAt": "2026-04-23T02:24:29.000Z",
      "State": "closed",
      "Comments": 0,
      "Reporter": "Contributor",
      "Rank": "recent",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Feature]: feat(mcp): expose orchestrate and batch workflows as MCP tools",
      "Excerpt": "### Problem or motivation\n\n## Problem\nThe CLI and Studio support publication-scale workflows (orchestrate, batch, plot-batch, sweep + reports), but the MCP server is still oriented around single diagram/plot generation and evaluation. Users who drive PaperBanana from Cursor / Claude Code / other MCP clients cannot kick off a full figure package or a manifest-based batch without leaving the agent context or shelling out manually.\n\n## Goal\nExtend the MCP server so IDE agents can invoke the same or",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/179",
      "PublishedAt": "2026-04-21T18:48:17.000Z",
      "State": "closed",
      "Comments": 0,
      "Reporter": "Contributor",
      "Rank": "top",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Feature]: Add native comparative evaluation for statistical plots",
      "Excerpt": "### Problem or motivation\n\n## Problem statement\nPaperBanana supports comparative VLM-as-judge evaluation for methodology diagrams, but there is no equivalent first-class workflow for statistical plots. This creates a gap where plot generation exists but plot quality cannot be systematically evaluated with the same 4-dimension protocol and interfaces (CLI/Studio/MCP).\n\n## Why this matters\n- Prevents regression testing for plot outputs.\n- Makes prompt/model tuning for plot generation harder to val",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/150",
      "PublishedAt": "2026-04-13T11:46:45.000Z",
      "State": "closed",
      "Comments": 0,
      "Reporter": "Contributor",
      "Rank": "top",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Feature]: Support PDF files as methodology / evaluation context (with optional page ranges)",
      "Excerpt": "### Problem or motivation\n\n## Motivation\nResearchers usually work from PDF papers, not hand-maintained .txt excerpts. Today, using PaperBanana means manually copying the methods section (or other relevant pages) into a text file. That is extra friction, easy to get wrong (wrong section, truncated context), and does not match how people actually write and revise papers.\n\nAdding native PDF input lowers the barrier to trying the tool on real work, improves reproducibility of \"what text was in conte",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/108",
      "PublishedAt": "2026-03-23T03:40:18.000Z",
      "State": "closed",
      "Comments": 0,
      "Reporter": "Contributor",
      "Rank": "top",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Feature]: Add structured progress streaming across pipeline, CLI, and MCP",
      "Excerpt": "### Problem or motivation\n\n## Background / Motivation\nRight now, PaperBanana’s progress reporting is:\n- Human-oriented only: rich console messages in the CLI and generic logs from structlog.\n- Not machine-readable: UIs and higher-level tools cannot easily track fine‑grained status (phases, iterations, timings).\n- Not exposed via MCP: MCP clients (e.g., Cursor, Claude Code) only see the final image; they can’t surface live pipeline progress to users.\n-\nThis limits:\n- Building custom UIs (e.g., we",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/103",
      "PublishedAt": "2026-03-16T06:53:32.000Z",
      "State": "closed",
      "Comments": 2,
      "Reporter": "Contributor",
      "Rank": "top",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Feature]: Auto-generate figure captions from the final pipeline output",
      "Excerpt": "### Problem or motivation\n\n## Problem / Motivation\n\n`paperbanana generate` takes a user-supplied `--caption` (called `communicative_intent` in `GenerationInput`) as **input** to the pipeline. \nThis caption seeds the planner and stylist but is never transformed into a publication-ready figure caption — the kind that appears under a figure in a paper: *\"Figure 3: Overview of our proposed method. (a) The encoder maps...\"*.\nAfter the pipeline finishes, users still need to manually write this caption",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/98",
      "PublishedAt": "2026-03-12T16:50:19.000Z",
      "State": "closed",
      "Comments": 0,
      "Reporter": "Contributor",
      "Rank": "recent",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Feature]: Add save_prompts run artifacts to persist formatted agent prompts",
      "Excerpt": "### Problem or motivation\n\n## Problem\n- PaperBanana has a config knob output.save_prompts: true in configs/config.yaml, but prompts are not actually saved anywhere.\n- When debugging quality regressions or provider differences, users can’t easily reproduce what was sent to the VLM/image generator without digging through logs (and logs may not include full prompts).\n- \n## Motivation\n- Reproducibility: When a diagram/plot looks wrong, the most important debug artifact is the exact prompt that was s",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/76",
      "PublishedAt": "2026-03-06T07:06:44.000Z",
      "State": "closed",
      "Comments": 0,
      "Reporter": "Contributor",
      "Rank": "top",
      "Extractor": "github_issue"
    },
    {
      "Title": "[Bug]: MCP `generate_diagram` returns JPEG data with `image/png` media type",
      "Excerpt": "### What happened?\n\nWhen calling the `generate_diagram` MCP tool, the tool result includes a base64-encoded image declared as `image/png`, but the actual image data is JPEG. This causes the Anthropic API to reject the tool result with a validation error:\n\n```\nError: messages.N.content.0.tool_result.content.1.image.source.base64:\nThe image was specified using the image/png media type, but the image appears to be a image/jpeg image\n```\n\nThis error halts the agent after the first diagram, preventin",
      "SourceUrl": "https://github.com/llmsresearch/paperbanana/issues/58",
      "PublishedAt": "2026-02-26T12:15:40.000Z",
      "State": "closed",
      "Comments": 1,
      "Reporter": "External",
      "Rank": "top",
      "Extractor": "github_issue"
    }
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