What's Loaded

by stcmain

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About

Shows what is consuming your agent's context window before the conversation starts — skill descriptions ranked by token cost, duplicate skills, memory files and their imports, and every configured MCP server.

Details

Author
stcmain
Categories
AI

Setup

Install What's Loaded in your MCP client (Claude Desktop, Cursor, Windsurf, and others).

Repository: https://github.com/stcmain/whats-loaded-mcp

Follow the installation instructions in the repository README, then restart your MCP client.

Your context window is already half spent before you type.An MCP server that shows what is consuming it — every skill description, every memory file and its hidden@imports, every configured MCP server — ranked by cost, with duplicates called out.

Skills are cheap to install and permanently expensive to keep. A skill'snameanddescriptiongo into the system prompt ofevery session, forever— only the body is loaded on demand. Install a few hundred and you've quietly mortgaged half your window before the first message.

Nothing surfaces this. You notice it as sessions that compact sooner than they used to, and you have no idea which of the things you installed six months ago is responsible.

# Context budget — cost before you type a single character ~95,316 estimated tokens always loaded. For scale: ~47.7% of a 200K window, ~9.5% of a 1M window. | Source | Count | Est. tokens | |----------------------------------------------|------:|-------------:| | Skill descriptions | 1,767 | 92,884 | | Memory files (CLAUDE.md/AGENTS.md + imports) | 3 | 2,432 | | MCP servers configured | 10 | not measured | | Total measurable | | 95,316 | > 79 duplicate skill names are costing ~9,435 tokens.

79 names were installed more than once — the same skill picked up from several sources, likeagent-browserappearing three times from three authors. But a shared name does not always mean a redundant copy: on that install only34 groups were byte-identical(~1,523 tokens genuinely recoverable), while45 shared a name and differed in content— different work wearing the same label, where deleting a copy loses something.duplicate_skillscompares content hashes and reports those two groups separately, so the cleanup advice is safe to act on rather than merely impressive.

Claude Desktop (one-click, no terminal):download the latestwhats-loaded-mcp-<version>.dxtfromReleasesand open it with Claude Desktop (double-click, or Settings → Extensions → Install Extension…). The server and its dependencies ship inside the bundle — no npm, no Node install.

Register with Claude Code (available in every session):

claude mcp add --scope user whats-loaded -- npx -y whats-loaded-mcp
{ "mcpServers": { "whats-loaded": { "command": "npx", "args": ["-y", "whats-loaded-mcp"] } } }
git clone https://github.com/stcmain/whats-loaded-mcp.git cd whats-loaded-mcp npm install && npm run build # then point your client at node /path/to/whats-loaded-mcp/dist/index.js

Published aswhats-loaded-mcpon npm and asio.github.stcmain/whats-loaded-mcpin theMCP Registry.

One optional setting, and it takes no credentials.

Personal and plugin scopes are read from~/.claudeeither way. Setting this is worth it when a desktop client launches the server: the process inheritsthat client'sworking directory, which is rarely the project you are asking about.

{ "mcpServers": { "whats-loaded": { "command": "npx", "args": ["-y", "whats-loaded-mcp"], "env": { "WL_PROJECT_ROOT": "/path/to/the/project" } } } }

What it counts, and what it refuses to guess

Getting this wrong in the flattering direction would be easy, so the accounting is deliberately conservative:

- Skill descriptions only.name+descriptionis what loads at startup. The body of aSKILL.mdis fetched on demand and isnotcounted, even though it is 100× larger.
- Enabled plugins only.Skills belonging to disabled plugins sit on disk costing nothing, and are reported separately rather than folded into the total.
- One version per plugin.The plugin cache keeps several content-hash copies of the same plugin; only the live one is counted. Counting them all would inflate the total and invent duplicates you cannot act on.
- marketplaces/is never counted.That tree is a git checkout of marketplacesource, not installed content.

- Token counts are estimates(~4 chars/token). Real counts depend on the tokenizer; prose runs lighter, code and CJK run denser. Treat the numbers as a ranking and a rough scale, not as billing. Anthropic's tokenizer is not public, so nothing local can do better than an estimate.
- MCP tool definitions are not measured.They can be a large share of your context, but measuring them means launching every server and enumerating its tools. This server does not launch anything, so it reports the server count and says so rather than guessing.
- JSON configs only.TOML-based clients (Codexconfig.toml) are not parsed.
- Client-specific.Built around the Claude Code layout (~/.claude). The MCP inventory reads Claude Desktop, Cursor, Windsurf and VS Code configs too, but skill accounting is Claude Code's model.
- It reports; it does not edit.Nothing is deleted, disabled or rewritten. Acting on the findings is your call.

This server's output goes straight into a model's context, so the interesting risk is not what it does to your machine — it is what it hands to the model.

- No child processes. No network.It only reads files. There is noexec, no shell, and no outbound connection anywhere in the codebase.
- No model input ever becomes a path.Every path is derived fromhomedir()orcwd(). The only model-controlled parameters are a clamped integer and a substring matched in memory against names already collected. Path traversal is not possible because there is no path construction to traverse.
- Memory file contents are never read into the report.CLAUDE.mdroutinely contains private operational detail. This server reports size and the import graph, never a line of content.
- MCP environment values are never read.Config files are where people leave API keys in plaintext. Only variablenamesare emitted — never values, not even masked.
- Skill names and descriptions are emitted,which is a deliberate exception: they are already in the model's context by definition, so reporting them discloses nothing new.
- Bounded work: depth-capped directory walks, symlink-loop protection via realpath, and a file size ceiling.

Built byShift The Culture— we run a one-person company on AI agents and ship the tooling we needed ourselves. This server is free and MIT-licensed, no strings.

It has three siblings, all also free and MIT:

- whats-running-mcp— what isactuallyrunning on the box right now, instead of what an old transcript claims.
-
whats-inherited-mcp— what a checkout you did not write tells your agent to do: instruction files, hooks, and the MCP servers it declares.
-
whats-allowed-mcp— what your agent can do without asking you: merged permission rules, which settings file wins, and the rules your client accepts and then ignores.

- Agent Fleet Ops Kit($29) — the other failure modes of running three or four agents on one box: two sessions editing the same checkout, a dev server nobody owns (so the agent tests a different app than it edits), and MCP servers leaked from crashed sessions that hold ports and RAM for weeks. Prefer PayPal? Same kit onPayhip.
-
Agent Reliability Kit($29) — a Stop hook and two CLIs that block a turn when an agent claims "done" against a repo, URL, or build that was never actually checked. Prefer PayPal? Same kit onPayhip.

The server above stays free and MIT either way — it has no upsell in it, no telemetry, and no dependency on the paid kits.

This server is MIT and stays MIT. There is no pro edition, no telemetry, and nothing held back from the free build. Sponsorship is how the maintenance gets paid for without any of that changing.

No sponsors yet — the first slot is open.Company sponsors get their name or logo in this section, in the two sibling servers, and on the sponsor page. Tiers, exactly what the placement is, and what it explicitly does not buy:https://shifttheculture.media/sponsor

Individuals:https://paypal.me/ShiftTheCultureLLC— any amount, no perks, no tier.

Local-first agent memory: a plain-Markdown Obsidian vault is the source of truth, with a rebuildable DuckDB index for hybrid BM25 + vector + graph recall.

Persistent memory and semantic search for AI coding assistants across sessions

Give your agent a memory: shared, cited, tenant-isolated knowledge-graph memory for any MCP host. Grounded answers from a local-first June endpoint — abstains rather than guesses.

Decentralized persistent memory for AI agents — encrypted vault storage built on Walrus and Sui.

Persistent memory for AI assistants and coding agents across ChatGPT, Claude, Cursor, and other MCP-compatible tools.

Your portable AI memory vault — memories, skills & configs, shared across every AI tool.

Local Work Model for AI agents that learns from real outcomes.

Adaptive MCP memory system for AI applications. Learns which retrieval strategies work for your data, scores results using cognitive science models, builds a knowledge graph automatically, and validates every parameter change against real query history before adopting it. Patent pending.

Auditable, self-improving knowledge & memory for AI agents over MCP — citation-enforced answers and a replayable why-trace, self-hosted on Postgres.

Turns your task manager into agent memory: hybrid (RRF) retrieval over TickTick or an Obsidian vault via an adapter contract. MCP server + CLI, no vector DB to maintain.

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