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sai-roadmap-mcp

by saichintamani
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🚀 SAI Roadmap MCP

An MCP (Model Context Protocol) server exposing my certifications, projects, and AI engineering roadmap as callable tools — including a semantic search engine built entirely from scratch.

💻 Core Technologies & Animated Architecture

🌐 1. High-Level Ecosystem (MCP Server Topology)

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)

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 (v1.29.0), stdio transport, with a Python/NumPy subprocess powering the ML tool.

Related MCP server: A2A MCP Server

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):

  1. Corpus (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, 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

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

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

{
  "mcpServers": {
    "sai-roadmap": {
      "command": "node",
      "args": ["/absolute/path/to/sai-roadmap-mcp/src/index.js"]
    }
  }
}

Connecting to Claude Code

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

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