# MCP-DBLP Evaluation Results

**Date:** November 22, 2025
**Experiments:** 3 independent runs
**Citations per run:** 104 (obfuscated academic citations)

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## Summary (Averaged Across 3 Experiments)

| Category | Web | MCP-M | MCP-U |
|----------|-----|-------|-------|
| **PM** (Perfect Match) | 29.3 (28.2%) | 49.0 (47.1%) | 86.0 (82.7%) |
| **WP** (Wrong Paper) | 19.3 (18.6%) | 15.7 (15.1%) | 16.3 (15.7%) |
| **NF** (Not Found) | 31.3 (30.1%) | 1.3 (1.3%) | 1.7 (1.6%) |
| **IM** (Incomplete Metadata) | 12.3 (11.9%) | 38.0 (36.5%) | 0.0 (0.0%) |
| **IA** (Incomplete Authors) | 4.7 (4.5%) | 0.0 (0.0%) | 0.0 (0.0%) |
| **CM** (Corrupted Metadata) | 7.0 (6.7%) | 0.0 (0.0%) | 0.0 (0.0%) |

**Key Finding:** MCP-DBLP eliminates metadata corruption (CM: Web 6.7% → MCP 0%) and dramatically reduces Not Found errors (NF: Web 30.1% → MCP 1-2%).

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## Experiment Design

### Three Methods Compared

| Method | Description | MCP-DBLP |
|--------|-------------|----------|
| **Web** | Web search only (control) | Disabled |
| **MCP-M** | DBLP search + manual BibTeX construction | Enabled |
| **MCP-U** | DBLP search + direct BibTeX export | Enabled |

### Classification Categories

| Code | Definition |
|------|------------|
| **PM** | Perfect Match - correct paper, complete metadata |
| **WP** | Wrong Paper - different paper than ground truth |
| **NF** | Not Found - entry missing or marked NOT FOUND |
| **IM** | Incomplete Metadata - missing doi/pages/volume or abbreviated names |
| **IA** | Incomplete Authors - truncated author list |
| **CM** | Corrupted Metadata - WRONG values (not missing) |

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## Individual Experiment Results

### First Experiment

| Category | Web | MCP-M | MCP-U |
|----------|-----|-------|-------|
| PM | 26 (25.0%) | 36 (34.6%) | 83 (79.8%) |
| WP | 7 (6.7%) | 20 (19.2%) | 18 (17.3%) |
| NF | 46 (44.2%) | 2 (1.9%) | 3 (2.9%) |
| IM | 17 (16.3%) | 46 (44.2%) | 0 (0.0%) |
| IA | 5 (4.8%) | 0 (0.0%) | 0 (0.0%) |
| CM | 3 (2.9%) | 0 (0.0%) | 0 (0.0%) |

### Second Experiment

| Category | Web | MCP-M | MCP-U |
|----------|-----|-------|-------|
| PM | 36 (34.6%) | 76 (73.1%) | 89 (85.6%) |
| WP | 25 (24.0%) | 10 (9.6%) | 15 (14.4%) |
| NF | 24 (23.1%) | 1 (1.0%) | 0 (0.0%) |
| IM | 10 (9.6%) | 17 (16.3%) | 0 (0.0%) |
| IA | 1 (1.0%) | 0 (0.0%) | 0 (0.0%) |
| CM | 8 (7.7%) | 0 (0.0%) | 0 (0.0%) |

### Third Experiment

| Category | Web | MCP-M | MCP-U |
|----------|-----|-------|-------|
| PM | 26 (25.0%) | 35 (33.7%) | 86 (82.7%) |
| WP | 26 (25.0%) | 17 (16.3%) | 16 (15.4%) |
| NF | 24 (23.1%) | 1 (1.0%) | 2 (1.9%) |
| IM | 10 (9.6%) | 51 (49.0%) | 0 (0.0%) |
| IA | 8 (7.7%) | 0 (0.0%) | 0 (0.0%) |
| CM | 10 (9.6%) | 0 (0.0%) | 0 (0.0%) |

---

## Variance Analysis

| Category | Web StdDev | MCP-M StdDev | MCP-U StdDev |
|----------|------------|--------------|--------------|
| PM | 7.0 | 22.8 | 5.7 |
| WP | 10.7 | 5.1 | 1.7 |
| NF | 13.9 | 0.6 | 4.6 |
| IM | 4.0 | 17.8 | 0.0 |

**Observations:**
- **MCP-U is most consistent** (PM StdDev 5.7, IM StdDev 0.0) - unmediated export produces stable results
- **MCP-M has high PM/IM variance** (StdDev ~18-23) - depends on whether agent includes complete metadata
- **Web has high WP/NF variance** (StdDev 11-14) - web search results vary across runs
- **WP rate varies more for Web** (StdDev 10.7) than MCP methods (2-5)

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## Key Findings

### 1. MCP-DBLP Eliminates Metadata Corruption
- **Web:** 7.0 CM errors per experiment (6.7%)
- **MCP-M:** 0.0 CM (0.0%) - zero
- **MCP-U:** 0.0 CM (0.0%) - guaranteed zero

### 2. MCP-DBLP Dramatically Reduces Not Found Errors
- **Web:** 31.3 NF (30.1%)
- **MCP-M:** 1.3 NF (1.3%)
- **MCP-U:** 1.7 NF (1.6%)
- **Reduction:** ~95% fewer NF errors

### 3. Unmediated Export (MCP-U) is Most Reliable
- Highest PM rate: 82.7%
- Zero IM, IA, CM errors across all experiments
- Direct DBLP export guarantees correct metadata

### 4. Wrong Paper Rate is Method-Independent
- All methods: ~15-19% WP
- This reflects ambiguity in the input citations
- DBLP search can return wrong papers for vague queries

### 5. Similar NF Rates Across MCP Methods
- **MCP-U NF:** 1.7 (1.6%) vs **MCP-M NF:** 1.3 (1.3%)
- Both methods achieve very low NF compared to Web (30.1%)
- Slight difference due to handling of unfound papers:
  - **MCP-M:** Creates placeholder entries for unfound papers
  - **MCP-U:** Only exports entries where DBLP fetch succeeded
- Key metric: MCP-U achieves 82.7% PM with **zero IM/IA/CM** errors

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## WP Consistency Analysis

**8 citations consistently classified as WP by MCP-U across all 3 experiments:**
`4, 10, 38, 56, 70, 76, 80, 89`

These represent citations with genuine ambiguity where DBLP returns a different paper than the ground truth.

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## File Organization

### Raw Data
- `data/` - First experiment raw data
- `second/` - Second experiment raw data
- `third/` - Third experiment raw data
- `ground_truth/` - 104 verified reference papers

### Supplementary Materials
- `SUPP/` - Consolidated data for paper submission

### Classification Files
- `data/classification_corrected.csv` - First experiment
- `second/results_mcp_m/` - Second experiment MCP-M (re-run)
- `third/classification_final.csv` - Third experiment

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## Methodology

1. **Ground Truth:** 104 papers sampled from DBLP (stratified by year: 50% 2020-2025, 25% 2015-2019, 25% 2010-2014)
2. **Input:** Obfuscated citations with varying difficulty levels
3. **Execution:** Claude agent with/without MCP-DBLP access
4. **Classification:** Gemini Flash 2.5 with explicit rules, consolidated across 2 runs
5. **Verification:** Manual review of edge cases

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## Implications for Paper

1. **Primary result:** MCP-DBLP eliminates metadata corruption (CM: 6.7% → 0%)
2. **Secondary result:** ~95% reduction in Not Found errors
3. **Tertiary result:** MCP-U achieves 82.7% Perfect Match rate with zero IM/IA/CM
4. **Limitation:** ~15-19% Wrong Paper rate across all methods (query ambiguity)

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*Last updated: 2025-11-23*
