sai-roadmap-mcp

<div align="center"> <!-- Customized High-Tech AI Logo --> <img src="public/logo.png" alt="SAI Roadmap MCP Logo" width="220" height="220" style="border-radius: 20%; filter: drop-shadow(0 0 25px rgba(0, 255, 255, 0.6));" /> <br /> # 🚀 SAI Roadmap MCP **An [MCP (Model Context Protocol)](https://modelcontextprotocol.io) server exposing my certifications, projects, and AI engineering roadmap as callable tools — including a semantic search engine built entirely from scratch.** </div> --- ## 💻 Core Technologies & Animated Architecture <div align="center"> <img src="https://img.shields.io/badge/Node.js-43853D?style=for-the-badge&logo=node.js&logoColor=white" /> <img src="https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white" /> <img src="https://img.shields.io/badge/NumPy-013243?style=for-the-badge&logo=numpy&logoColor=white" /> <img src="https://img.shields.io/badge/Model_Context_Protocol-6B21A8?style=for-the-badge&logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgZmlsbD0ibm9uZSIgc3Ryb2tlPSJ3aGl0ZSIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwYXRoIGQ9Ik00IDEwaDEyIi8+PHBhdGggZD0iTTQgMTRoOSIvPjxwb2x5Z29uIHBvaW50cz0iMyAzIDEwIDEwIDMgMTcgMyAzIi8+PC9zdmc+&logoColor=white" /> <img src="https://img.shields.io/badge/Claude_Desktop-D97757?style=for-the-badge&logo=anthropic&logoColor=white" /> </div> <br/> ### 🌐 1. High-Level Ecosystem (MCP Server Topology) ```mermaid graph TB %% Core Styling with High-Contrast Cyber Theme classDef client fill:#0f172a,stroke:#38bdf8,stroke-width:3px,color:#fff,stroke-dasharray: 5 5 classDef server fill:#1e1b4b,stroke:#a855f7,stroke-width:3px,color:#fff classDef python fill:#064e3b,stroke:#34d399,stroke-width:3px,color:#fff classDef data fill:#451a03,stroke:#fbbf24,stroke-width:3px,color:#fff %% Clients subgraph Clients [MCP Clients] ClaudeDesk[Claude Desktop App]:::client ClaudeCode[Claude Code CLI]:::client end %% Node Server subgraph NodeServer [Node.js MCP Server] StdioTransport((stdio / JSON-RPC 2.0)):::server ToolRouter{Tool Router}:::server ProfileTool[get_profile]:::server CertTool[get_certifications]:::server ProjTool[get_projects]:::server RoadmapTool[get_roadmap]:::server SearchTool[semantic_search]:::server end %% Python Backend subgraph PythonEngine [Python ML Subprocess] QueryParser[LSA Query Parser]:::python LSAWeights[(lsa_model.npz)]:::data MergeSet[(Bigram Merge Set)]:::data PortfolioData[(data.json)]:::data end %% Flow Mapping Clients == "stdio (JSON-RPC)" ==> StdioTransport StdioTransport --> ToolRouter ToolRouter --> ProfileTool ToolRouter --> CertTool ToolRouter --> ProjTool ToolRouter --> RoadmapTool ToolRouter ==> SearchTool SearchTool -.->|Spawns Child Process| PythonEngine QueryParser <--> LSAWeights QueryParser <--> MergeSet QueryParser <--> PortfolioData ``` ### ⚡ 2. Animated Interaction Sequence (Semantic Search ML Pipeline) ```mermaid sequenceDiagram autonumber participant U as User Query (MCP Client) participant N as Node.js Server participant P as Python LSA Engine (query.py) participant C as Corpus Builder participant M as Truncated SVD Math Note over C, M: Build Time (npm run train) C->>C: Extract 51 Sentences from Portfolio C->>C: Calculate PMI & Merge Bigrams (min_count=3) C->>M: Apply Explicit TF-IDF Weighting M->>M: Compute numpy.linalg.svd (Truncated) M-->>C: Save lsa_model.npz & Bigrams Note over U, P: Runtime Execution U->>N: Call `semantic_search` activate N N->>P: Spawn process with query args activate P P->>P: Apply identical Bigram merges to query P->>P: Project Query into Latent Space P->>P: Compute Cosine Similarity vs Corpus P-->>N: Return JSON matching documents deactivate P N-->>U: Return Formatted Result to Claude deactivate N ``` --- Built on the official [`@modelcontextprotocol/sdk`](https://github.com/modelcontextprotocol/typescript-sdk) (v1.29.0), stdio transport, with a Python/NumPy subprocess powering the ML tool. ## Tools | Tool | Description | Optional Input | |---|---|---| | `get_profile` | Basic profile info | — | | `get_certifications` | Certifications, filterable by skill | `skill: string` | | `get_projects` | Portfolio projects, filterable by tech stack | `tech: string` | | `get_roadmap` | 2026 learning roadmap, filterable by quarter | `quarter: "Q1"–"Q4"` | | `semantic_search` | Real semantic retrieval, not keyword matching | `query: string`, `top_n: number` | ## The semantic search engine **Pipeline** (full implementation in [`src/ml/lsa.py`](src/ml/lsa.py)): 1. **Corpus** ([`src/ml/corpus.py`](src/ml/corpus.py)) — 51 natural-language sentences generated from certifications, detailed project descriptions, and roadmap entries. 2. **Bigram phrase detection** — pointwise mutual information (PMI), the same idea behind word2vec's original `word2phrase` tool. Merges tightly-bound word pairs (`machine_learning`, `medireach_ai`, `artificial_intelligence`) into single tokens before training. 3. **TF-IDF weighting** — term frequency × smoothed inverse document frequency, computed explicitly. 4. **Truncated SVD** — `numpy.linalg.svd`, manually truncated to the top-*k* singular vectors (the actual Latent Semantic Analysis step — not `sklearn.fit_transform()`). 5. **Cosine similarity** in the resulting latent space ranks documents against a query. ### Two things I got wrong on the first pass, and fixed **Bigram threshold, take one:** my first PMI threshold (`min_count=2, threshold=3.0`) merged **147 bigrams** — almost all of them grammatical glue like `of_the`, `by_google`, `is_a`, not real phrases. The bug: with only 51 sentences, raw PMI is noisy, and stopwords weren't excluded from forming pairs. Fixed by excluding stopwords from bigram formation and raising the bar to `min_count=3, threshold=15.0` — now produces 19 bigrams, and every single one is a genuine phrase (`machine_learning`, `google_cloud`, `vibe_coding`, `iit_bombay`). **Query/training vocabulary mismatch:** after adding bigram merging, queries were still being tokenized with plain word-splitting — so a query like "machine learning" stayed as two tokens while the trained vocabulary only had the merged `machine_learning`. Silent mismatch, no error thrown, just quietly worse retrieval. Fixed by persisting the learned merge set alongside the model and applying it identically at query time (`apply_bigram_merges()` in `corpus.py`). ### Quantitative evaluation — precision@k Most small ML side-projects show a few queries that "look like they work." `src/ml/evaluate.py` instead defines 10 hand-labeled queries with explicit relevance judgments and reports precision@k: ``` Query P@1 P@3 P@5 ---------------------------------------------------------------------- python certifications 1.00 1.00 0.80 cloud computing certifications 1.00 0.67 0.40 multi agent healthcare assistant 1.00 1.00 1.00 edtech platform for students 1.00 1.00 1.00 frontend animation and design 0.00 0.00 0.00 hackathon and competition experience 0.00 0.00 0.00 SQL and database skills 0.00 0.00 0.00 generative AI and large language models 1.00 0.67 0.40 deep learning quarter in the roadmap 0.00 0.67 0.40 agentic IDE development tools 1.00 1.00 0.60 ---------------------------------------------------------------------- MEAN 0.60 0.60 0.46 ``` **Three queries scored zero — here's exactly why**, diagnosed rather than hand-waved: - `"frontend animation and design"` → query contains "animation" (singular); the corpus only ever says "animations" (plural). Out-of-vocabulary, no signal. This is the classic bag-of-words weakness — no stemming, no lemmatization. - `"hackathon and competition experience"` → same issue: "competition" and "experience" never appear in the corpus at all (it says "Hackathon," not "competition"). - `"SQL and database skills"` → no OOV words here, but a genuine ranking failure: `idf("skills") == idf("database")` (both 3.833) because both happen to appear in exactly one document — IDF can't tell "rare and topically specific" apart from "rare by coincidence" at this corpus size. The word "skills" then drags the ranking toward the wrong (but skills-heavy) document. These are real, well-understood limitations of small-corpus bag-of-words retrieval, not implementation bugs — and documenting them precisely is more useful than a misleadingly clean demo. ### Revisiting word2vec with the bigger corpus I expanded the corpus 3× (17 → 51 sentences) partly to test whether word-level skip-gram (still in [`src/ml/word2vec.py`](src/ml/word2vec.py), Mikolov et al. 2013, negative sampling, full from-scratch NumPy training) would become viable. Honest result: **no.** ``` 'cloud' -> [('computing', 0.746), ('oac', 0.728), ('analytics', 0.698), ('infrastructure', 0.645)] ← coherent 'python' -> [('generative_ai', 0.684), ('workflow', 0.599), ('sql', 0.595)] ← noise 'agents' -> [('good', 0.687), ('intensive', 0.681), ('5', 0.636)] ← noise ``` "cloud" produces a genuinely sensible neighborhood; "python" and "agents" still don't. 51 sentences is closer to viable than 17 was, but word2vec realistically needs thousands of sentences minimum. LSA remains the correct choice for this corpus size — confirmed by actually re-running the experiment, not just assumed. ## Setup ```bash git clone https://github.com/saichintamani/sai-roadmap-mcp.git cd sai-roadmap-mcp npm install pip3 install -r requirements.txt --break-system-packages npm run train # builds corpus, detects bigrams, trains LSA model npm run evaluate # runs the precision@k evaluation above ``` ## Running standalone ```bash npm start ``` Communicates over stdio via JSON-RPC 2.0. Ready message goes to stderr; stdout is reserved for protocol messages. ## Connecting to Claude Desktop ```json { "mcpServers": { "sai-roadmap": { "command": "node", "args": ["/absolute/path/to/sai-roadmap-mcp/src/index.js"] } } } ``` ## Connecting to Claude Code ```bash claude mcp add sai-roadmap -- node /absolute/path/to/sai-roadmap-mcp/src/index.js ``` ## Repo structure ``` sai-roadmap-mcp/ ├── src/ │ ├── index.js # MCP server: 5 tools, stdio transport │ ├── data.json # Structured portfolio data │ └── ml/ │ ├── corpus.py # Sentence generation + PMI bigram detection │ ├── word2vec.py # Skip-gram + negative sampling (kept, documented as non-viable here) │ ├── lsa.py # TF-IDF + truncated SVD -- the model actually used │ ├── train.py # Trains and saves the model + merge set │ ├── query.py # CLI query interface, called by index.js │ ├── evaluate.py # Precision@k evaluation against hand-labeled queries │ └── lsa_model.npz # Pre-trained weights ├── requirements.txt ├── package.json └── README.md ``` ## Why this exists Most student AI portfolios show *using* an LLM API. This shows three different things: understanding of the **MCP protocol layer** production AI tools run on; a working **classical NLP/ML retrieval system** built from first principles; and — maybe more importantly — the engineering discipline to **measure it, find the failure modes, and document them precisely** instead of cherry-picking examples that look good. ## License MIT