fledgling-mcp 0.7.0

MCP tools that help AI agents get their bearings in a codebase — unified SQL views over code, git, docs, and conversations, powered by DuckDB.

Fledgling

MCP tools that help AI agents get their bearings in a codebase — unified SQL views over code, git, docs, and conversations, powered by DuckDB.

Three ways to run:

# Zero-dependency MCP server (pure DuckDB, no Python)
curl -sL https://raw.githubusercontent.com/teaguesterling/fledgling/main/sql/install-fledgling.sql | duckdb

# Python API
pip install fledgling-mcp
python -c "import fledgling; fledgling.connect().find_definitions('**/*.py').show()"

# FastMCP server with smart defaults, caching, and compound workflows
pip install fledgling-mcp[pro]
fledgling-pro

Before and After

Your agent wastes tokens parsing text. Fledgling gives it purpose-built tools that return structured data.

Find a function definition

Before — grep returns raw text the agent has to parse:

grep -rn 'def parse_config' src/
src/config.py:42:def parse_config(path: str, strict: bool = False) -> Config:

After — the agent calls FindDefinitions:

FindDefinitions(file_pattern="src/**/*.py", name_pattern="parse_config%")

| file_path     | name         | kind                | start_line | end_line | signature                                   |
|---------------|--------------|---------------------|------------|----------|---------------------------------------------|
| src/config.py | parse_config | DEFINITION_FUNCTION | 42         | 68       | def parse_config(path: str, strict: ...) -> |

Compose queries across domains

-- Functions in recently changed files, ranked by cyclomatic complexity
SELECT * FROM changed_function_summary('HEAD~3', 'HEAD', 'src/**/*.py')

Code analysis + git history in one call. No shell pipelines, no string parsing.

What's Included

MCP Tools (14)

Tool	What it does
ReadLines	Read file lines with range, context, and match filtering
FindDefinitions	AST-based search for functions/classes across 30 languages
FindInAST	Semantic code search: calls, imports, loops, conditionals, strings, comments
CodeStructure	Structural overview with cyclomatic complexity metrics
MDOverview	Browse all docs with keyword/regex search
MDSection	Read a specific markdown section by ID
GitDiffSummary	File-level change summary between revisions
GitDiffFile	Line-level unified diff
GitShow	File content at a specific git revision
Help	Built-in skill guide with workflows and macro catalog
ChatSessions	Browse Claude Code conversation sessions
ChatSearch	Full-text search across conversation messages
ChatToolUsage	Tool usage patterns
ChatDetail	Deep view of a single session

Plus 20+ composable SQL macros via the query tool: complexity_hotspots, function_callers, module_dependencies, structural_diff, doc_outline, and more.

Fledgling Pro (FastMCP)

The fledgling[pro] package adds a FastMCP server with:

• Smart defaults — auto-detects your project's language, doc directory, and git branch
• Token-aware output — auto-truncation with hints ("use lines='N-M' to narrow")
• Compound workflows — explore, investigate, review, search in one call
• MCP resources — project overview, docs, git state always available without tool calls
• Prompt templates — context-aware exploration, investigation, and review workflows
• Session state — caching, access log, and kibitzer (suggests better tool usage)

Python API

import fledgling

con = fledgling.connect()

# Macros as methods — return composable DuckDB Relations
con.find_definitions("**/*.py", name_pattern="parse%").show()
con.recent_changes(5).select("hash, message").df()
con.code_structure("src/**/*.py").filter("cyclomatic_complexity > 5").show()

# Module-level for quick scripting
from fledgling.tools import find_definitions, recent_changes
find_definitions("**/*.py").show()

CLI for Humans

fledgling find-definitions 'src/**/*.py' '%parse%'
fledgling recent-changes 10 -c hash,message
fledgling CodeStructure '**/*.rs' -f csv
fledgling query "SELECT * FROM complexity_hotspots('**/*.py', 10)"
fledgling help
fledgling update   # preserves your module/profile config

Tab completion: eval "$(fledgling --completions bash)"

Install

Per-project (recommended)

curl -sL https://raw.githubusercontent.com/teaguesterling/fledgling/main/sql/install-fledgling.sql | duckdb

Creates .fledgling-init.sql, .fledgling-help.md, and .mcp.json in your project root. Customize modules and profile on the install page.

Via pip

pip install fledgling-mcp          # CLI + Python API
pip install fledgling-mcp[pro]     # + FastMCP server

Requirements

• DuckDB >= 1.5.0 (CLI for MCP server, Python package for API)
• Community extensions installed automatically

Architecture

┌─────────────────────────────────────────┐
│  fledgling-pro (FastMCP)                │  pip install fledgling-mcp[pro]
│  Smart defaults, caching, workflows,    │
│  prompts, kibitzer, resources           │
│                                         │
│  ┌───────────────────────────────────┐  │
│  │  fledgling (Python API)           │  │  pip install fledgling-mcp
│  │  fledgling.connect()              │  │
│  │  con.find_definitions().show()    │  │
│  │                                   │  │
│  │  ┌─────────────────────────────┐  │  │
│  │  │  SQL macros (DuckDB)        │  │  │  curl | duckdb
│  │  │  14 MCP tools               │  │  │
│  │  │  read_lines, sitting_duck,  │  │  │
│  │  │  duck_tails, duckdb_markdown│  │  │
│  │  └─────────────────────────────┘  │  │
│  └───────────────────────────────────┘  │
└─────────────────────────────────────────┘

The SQL macros are the foundation — pure DuckDB, zero Python dependency, sandboxed read-only. The Python API wraps them as composable Relations. The FastMCP layer adds coordination intelligence.

Development

git clone https://github.com/teaguesterling/fledgling.git
cd fledgling
pip install -e ".[pro]"
pip install duckdb pytest
pytest

523 tests across SQL macros, MCP integration, CLI, Python API, and FastMCP server.

Coming Soon

• fledgling-edit — AST-aware code editing with pattern matching and template substitution (design spec)
• Kit management — Quartermaster pattern: curated tool subsets per task type with model-aware configuration

Why "Fledgling"?

From the 1996 film Fly Away Home — a girl raises orphaned geese and teaches them their migration route by leading them with an ultralight aircraft. The geese imprint on her, learn the path, and eventually fly it on their own.

Fledgling gives AI agents structured tools so they can learn to navigate your codebase. A fledgling is a young bird learning to fly. This tool is what gets it airborne.

License

Apache-2.0