profile-project
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@profile-projectinit this project"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
profile-project
A self-contained Claude Code plugin that runs a fixed, agent-driven DAG to profile a project — source code, in-repo docs, transcripts, notes, and external references — and emit three durable deliverables:
Agent-facing context pages (
profile/context/) — dense, multi-page markdown for fast machine consumption.A human/developer guide (
profile/guide/) — readable narrative documentation for onboarding.A local queryable vectorstore over both, answered via
pp_query.
It ships as one Python FastMCP server (profile_project) plus a skill suite and slash
commands. It runs per-session over stdio, needs no Docker and no mandatory
Ollama, and never writes a single local artifact to a target project until that
project is explicitly initialized via /profile-project:init.
Installation
profile-project is launched by Claude Code over stdio. The host runs the server with:
uv run --directory ${CLAUDE_PLUGIN_ROOT} python -m profile_projectuv syncs the environment from the root pyproject.toml, then runs the package as a
module. You need only uv and Python ≥ 3.11 on the machine.
Dependency extras
The base install is light. Backends are optional extras (a missing extra for a selected backend is a warn + disable, never a crash):
Extra | Pulls in | Required for |
(base) |
| server, config, DAG, chunking, ollama (httpx only) |
|
| ChromaDB local store |
|
| Pinecone remote store |
|
| OpenAI embeddings |
|
| sentence-transformers (the default embedder) |
| (httpx only — already base) | Ollama embeddings |
| union of the above | everything |
Recommended default backend. The recommended path is sentence-transformers
(local, offline after first model pull, dim 384) for embeddings + chromadb (local
on-disk) for storage — no Docker, no external service, no API key. For a manual/dev
checkout, install it with [local-embeddings] and [chroma] (or [all]):
uv pip install -e ".[local-embeddings,chroma]"Enabling the vectorstore on a plugin install (opt-in)
The server always starts on a plain install, but the vectorstore is off by
default: the stdio launch command (uv run … python -m profile_project) installs only
the base dependencies, so the embedding + store libraries are absent and the conflict
matrix warns + disables the vectorstore (the DAG still runs and produces both guides).
This keeps cold start fast — sentence-transformers pulls in torch (~2 GB), which is
too heavy to download on every launch.
To turn the vectorstore on, add the extras to the launch command in the installed
plugin's .mcp.json (${CLAUDE_PLUGIN_ROOT}/.mcp.json):
{
"mcpServers": {
"profile-project": {
"command": "uv",
"args": ["run", "--extra", "local-embeddings", "--extra", "chroma",
"--directory", "${CLAUDE_PLUGIN_ROOT}", "python", "-m", "profile_project"]
}
}
}The first launch after adding the extras resolves and downloads them (slow, one
time); later launches reuse the synced environment. Swap in --extra pinecone /
--extra openai (or --extra all) for the corresponding backends. Every vectorstore
backend needs at least one extra; only ollama embeddings run on the base install (httpx
is a base dependency) — but a store backend (chroma or pinecone) is still required.
Related MCP server: Claude Persistent Memory
Initialization
The plugin must be explicitly initialized per project before it writes any local artifact — this gate is enforced in the MCP server, not just the skill.
/profile-project:initinit runs read-only diagnostics (pp_config_validate, pp_vectorstore_check),
collects/confirms config, validates that required secrets exist in your environment, then
calls the server tool pp_init_project, which transactionally writes
.profile_project_config.json, the gitignored .profile_project/ tree, the
.initialized stamp, and the .gitignore entry. Until init succeeds, every mutating
tool refuses with a structured not_initialized error and writes nothing.
Re-run /profile-project:init any time to reconfigure (idempotent); use
/profile-project:init --reinit to overwrite/reset existing run artifacts.
Usage
Command | What it does |
| Initialize the project (the only path that creates initial artifacts) |
| Run the full profiling DAG |
| Show run/phase status and what's next |
| Ask a semantic question over the profile |
| Browse generated pages and per-phase artifacts |
| Incrementally refresh an existing profile |
A typical first run: /profile-project:init → /profile-project:profile →
/profile-project:query "how is config resolved?". The generated profile/context/ and
profile/guide/ directories are committable project artifacts.
Configuration
Configuration is layered. Project JSON overrides env (the inverse of
agent-knowledgebase): .profile_project_config.json at the project root takes precedence
over PROFILE_PROJECT_* environment variables, which act as defaults.
Precedence (highest to lowest): init kwargs → project JSON → env → .env → file secrets
→ field defaults.
Secrets are environment-only. PROFILE_PROJECT_OPENAI_API_KEY and
PROFILE_PROJECT_PINECONE_API_KEY are modeled as SecretStr, read only from the
environment, and are never written to .profile_project_config.json (the JSON source
hard-rejects forbidden keys), never stamped into chunk metadata, and never logged
(masked). .env.example documents the env vars and contains no real values.
Common env vars (full table in .env.example):
Env var | Maps to | Default |
|
| (unset → init prompts) |
|
| (unset → init prompts) |
|
|
|
|
| (none) |
|
| (none) |
Embeddings. The default embedder's canonical geometry id is
sentence-transformers/all-MiniLM-L6-v2@hf-fp32 (stamped per chunk so a query-time
rebuild rejects a mismatched embedder rather than returning meaningless scores). OpenAI
and Ollama are alternatives selected via config.
Pinecone uses an EXISTING index only. The plugin never creates a Pinecone index.
You supply an existing index ref + embeddings_model; at connect time the store
validates index.dimension == effective_embedding_dim and refuses on mismatch. A missing
index ref, missing key, or dimension mismatch warns + disables the vectorstore (the
DAG still runs and produces the guides).
Troubleshooting
"not_initialized" on a tool call. Run
/profile-project:initfirst. The gate is server-enforced; no mutating tool writes before initialization.Vectorstore silently disabled. Run
pp_config_validateandpp_vectorstore_check; a missing extra ([chroma]/[pinecone]/[openai]/[local-embeddings]), missing API key, missing Pinecone index ref, unreachable Ollama host, or dimension mismatch all warndisable rather than crash. The warning names the exact cause. On a plugin install the most common cause is that the launch command installs only base deps — see Enabling the vectorstore on a plugin install above to add the
--extraflags.
pp_queryreturnsindex_disabled/index_empty. The vectorstore is off, or no vectors have been built yet — run/profile-project:profile(which runsbuild_vectorstore) or checkpp_index_status."project_root_moved". The project was initialized for a different absolute root. Run
/profile-project:init --reinit.Garbled JSON-RPC / protocol errors. Nothing may be written to stdout under stdio transport — all logs go to stderr only. If you patched the server, ensure no
print()reaches stdout.First profiling run is slow. sentence-transformers downloads
all-MiniLM-L6-v2once; subsequent runs are offline.
Security & hygiene
Secrets are env-only (
SecretStr): never in.profile_project_config.json, never in chunk metadata, never logged (masked)..profile_project/is gitignored (local store, run-state, artifacts, cache,.initialized). Theprofile/guides are intentionally committable.No user-specific absolute paths are written to tracked config;
profile.root_diris resolved at runtime, never persisted.stdio hygiene: logs go to stderr only; stdout stays clean for JSON-RPC framing.
No remote provisioning: Pinecone indexes are never auto-created, so the plugin cannot silently incur cost.
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