This is a token-optimization MCP server that reduces LLM prompt sizes by stripping redundancy, compressing tool results, and managing context, without modifying the underlying model.
Tools:
slimtoken.optimize_messages: Minify a full request (messages, system prompt, tool definitions) while preserving structure and tool-call validity. Supports Anthropic, OpenAI, and Ollama formats.
slimtoken.estimate_tokens: Count tokens in a request body using the bundled offline cl100k_base tokenizer, returning total and per-message breakdowns.
slimtoken.prune_context: RAG-style context pruning that retrieves relevant warm entries, summarizes old turns, and enforces a token budget, returning a ready-to-inject <cold_memory>/<recent_context> prompt block.
slimtoken.minify_tool_result: Lossy, type-aware compression of large tool_result content (directory listings, logs, JSON, source code, etc.) into a compact representation with a metadata header.
slimtoken.inspect_budget: Read-only analysis of token usage across system, tools, and messages; reports headroom against a budget and whether leading messages would be pruned.
slimtoken.get_config: Returns the active MinifyConfig built from SLIMTOKEN_* environment variables, showing enabled stages and parameters.
slimtoken.list_model_presets: Lists recommended local-model configurations by GPU VRAM tier (4/8/16 GB), optionally with live-measured token reduction percentages.
slimtoken.high_context_presets: Shows high-context dense and MoE model configurations per VRAM tier, including the largest nominal context and effective context window after compression.
Optimizes requests to Ollama's local API by reusing the OpenAI conversion to normalize to Anthropic's format, applying token minification, and converting back, reducing input tokens for local model inference.
Optimizes requests to the OpenAI API by converting bodies to Anthropic's canonical format, running the token-minification pipeline, and converting back, thereby reducing input tokens for OpenAI-compatible endpoints.
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., "@slimtoken Can you optimize this request to use fewer tokens?"
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.
Every LLM call spends tokens — on a bloated system prompt, a tool schema you wrote twice, an old turn that no longer matters, "Sure!" at the head of every reply. slimtoken rewrites the request before it leaves your machine so the model sees less, charges less, and answers faster.
It's a small Python toolkit that runs three ways: as an always-on proxy in front of any Anthropic / OpenAI / Ollama backend, as an MCP server any agent can call, or imported as a plain library. Same code, same wins either way.
Alongside the request-body minify pipeline, slimtoken ships prompt reframe
— a 1-ms CPU pass that takes a rambling 200-word user prompt and returns a
tight 25-word instruction, with the original intent preserved by construction
(no LLM roundtrip). It's exposed as slimtoken.prompt_reframe, an MCP server
(slimtoken-reframe-mcp), and an Agent Skill (skills/prompt-reframe/).
A measured walkthrough: a one-table token-savings proof, the per-stage pipeline
at a glance, a worked before/after, install + first-call instructions, and links
to the deep reference. MIT-licensed; ships with orjson, xxhash, and
tiktoken so the token counts below are real, not guesstimates.
Every number below is computed by slimtoken's own real cl100k tokenizer on
representative payloads. Run them yourself with slimtoken presets --measure.
The request-side pipeline (tools · system · messages · dedup · distill ·
budget) runs on every request by default and is lossless: it only minifies
whitespace, stubs byte-identical duplicate tool results, distills assistant
prose beyond the keep-last window, and hard-prunes only when the budget is
exceeded. Old user turns (requirements, schemas, constraints) are preserved
verbatim. The lossy stages — tool_compress (type-specific tool-result
reduction) and minify_dom (HTML pruning) — are opt-in, off by default.
Reduction scales with how much waste the session carries:
Scenario | Before | After | Reduction |
Typical coding session (a file re-read 3×, verbose turns) | 772 tok | 507 tok | −34.3% |
Bloated session (6 repeated file reads + verbose history) | 3 312 tok | 1 187 tok | −64.2% |
HTML dump session (10 scraped pages) | 10 894 tok | 1 768 tok | −83.8% |
The response-side filter strips lead-in filler ("Sure!", "Here is the code:",
"Let me know if you need anything else.", …) from the streamed head. On by
default — set SLIMTOKEN_FILLER=0 to disable. The token cap
(SLIMTOKEN_MAX_TOKENS) and stop sequences (SLIMTOKEN_STOP) are opt-in.
Scenario | Before | After | Reduction |
Short reply with filler lead-in | 44 tok | 38 tok | −13.6% |
Long reply with filler lead-in | 380 tok | 374 tok | −1.6% |
Clean reply (no filler) | 21 tok | 21 tok | 0% (nothing to strip) |
Input reduction + output reduction together, on the same session:
Scenario | Before | After | Reduction |
Typical session | 793 tok | 528 tok | −33.4% |
Bloated session | 3 692 tok | 1 561 tok | −57.7% |
HTML dump session | 10 938 tok | 1 806 tok | −83.5% |
The honest caveat: reduction is proportional to waste. A clean, short session with no repeated content and no filler gets ~0% — slimtoken never invents savings. The more a session re-reads files, repeats tool output, or carries verbose old turns, the more it saves. That's the point: it removes redundancy, not meaning.
Related MCP server: everything-slim
The proxy is the default. It sits in front of your model API and minifies every request automatically — the agent can't skip it, so you get the savings without relying on the model to remember to call anything.
# Local build — Cython-compiled by default (skips gracefully to pure Python)
git clone https://github.com/greyok00/slimtoken
cd slimtoken && ./scripts/install.sh
# or, from PyPI:
pip install slimtoken
# Default setup — the proxy (one command, reversible)
slimtoken install
slimtoken serve --upstream http://127.0.0.1:8080 # local llama-server
# or: slimtoken serve --upstream https://api.anthropic.com # cloud
# `slimtoken install` already wired ANTHROPIC_BASE_URL to the proxy.
# Point your client at it and every request is minified automatically.
# Nothing else to do.
# Alternatives (not the default — on-demand only):
# CLI: slimtoken optimize -i request.json
# MCP: slimtoken-mcpslimtoken install writes a marker block to your shell rc that exports
ANTHROPIC_BASE_URL to the proxy (prior value backed up to ~/.slimtoken/prev_env
and restored on uninstall). It never touches settings.json, CLAUDE.md, or
mcp.json, so removal is clean and fully reversible: slimtoken uninstall.
The proxy is the only surface that guarantees every request is minified. MCP tools and the CLI are on-demand — the agent has to choose to call them, and a busy agent will forget. The proxy rewrites the request at the API layer, so the savings happen whether or not the agent "remembers" slimtoken. If you want the token reduction to actually happen, route through the proxy.
Some setups don't want automatic minification (exact-fidelity debugging, or a model that must see raw tool output verbatim). Opt out cleanly:
Per-request passthrough: SLIMTOKEN_MINIFY=0 — raw passthrough, no rewrite.
Don't route through it: unset ANTHROPIC_BASE_URL (or point it at your
model directly) — the proxy only sees traffic that's sent to it.
Full removal: slimtoken uninstall — restores your prior
ANTHROPIC_BASE_URL and removes the marker block.
A minify pipeline runs on each request, all on by default. The diagram shows the
request lifecycle with the t0–t4 latency boundaries the proxy records per
request — proxy-side work (ingress + optimize) is what slimtoken controls;
model-side (forward → first token → final token) is where real time goes.
sequenceDiagram
participant C as client
participant P as slimtoken proxy
participant B as backend / model
C->>P: POST /v1/messages (t0)
P->>P: read request (t0→t1)
P->>P: minify: tools · system · messages · dedup · distill · budget (t1→t2)
P->>B: forward minified body (t2)
B-->>P: first output token (t3)
P-->>C: stream raw bytes back (t3→t4)
Note over P: proxy-side = (t1-t0)+(t2-t1) ≈ 12 ms<br/>model-side = (t3-t2)+(t4-t3) — dominatesStage | What it does | Lossy? |
🧰 tools | Drop | no |
📋 system | Collapse whitespace and duplicate banner lines outside code fences; preserve | no |
💬 messages | Collapse blank-line runs and trailing whitespace in text blocks; pass | no |
🔄 dedup | Collapse repeated | no* |
📝 distill | Truncate old assistant prose beyond the last | assistant old turns only |
🎯 budget | Hard token cap ( | drops oldest |
🌐 dom (opt-in) |
| yes |
🗜️ tool_compress (opt-in) |
| yes |
* dedup is lossless in practice — the latest copy is always kept verbatim; only stale duplicates are stubbed.
Safety guarantees — fenced code blocks (triple-backtick / ~~~) preserved
byte-identical; pruning is pair-safe (a tool_result is never orphaned from its
tool_use); identity-based change detection returns unchanged content zero-copy;
the grammar field is stripped from request bodies.
The proxy above rewrites request bodies (tools, system, message history) on their way to a model. That's a different problem from tightening a single user prompt. If you're about to spend tokens on a rambling 200-word request that could be a sharp 25-word instruction, you'll want a rewriter first.
slimtoken.prompt_reframe is that rewriter. Pure CPU, no model roundtrip;
~1 ms per call. Five stages, called individually or as the bundled
frame_prompt pipeline:
flowchart LR
A[raw user prompt] --> B[classify_domain]
B --> C[reframe_prompt<br/>strip filler + dedupe]
C --> D[shrink_prompt<br/>TextRank-lite<br/>cap to word budget]
D --> E[minify_prompt<br/>cosmetic squeeze]
E --> F[build_system<br/>tight declarative system]
F --> G[tight prompt + system]Stage | What it does |
🏷️ | Keyword match into {business, professional, osint, cybersecurity, code, general}. Used to pick the right domain hint when composing the system prompt. |
🧽 | Strip 30+ conversational filler phrases ( |
✂️ | Rank sentences by relevance + length and pack the top-N until the word budget is met. Modes: |
🪶 | Collapse whitespace; drop redundant punctuation runs. Cosmetic only. |
🛠️ | Compose a tight system prompt from role + style + domain hints + up to 6 rules. Single short line so it doesn't waste context. |
Why TextRank-lite, not an LLM? It can't drop intent. The output is built from sentences that already appear in the user's prompt, ranked by their overlap with the prompt itself. A separate small LLM could paraphrase — but it costs a roundtrip and can quietly lose a detail. Use the reframe for intent-preserving shrink; pair it with an LLM only when you actually want a paraphrase.
Worked example:
Input |
|
Can you basically just tell me what is the answer really kind of like basically please help me with this. | Tell me what is the answer. |
1109 chars / 208 words / ~10 sentences about a Q3 revenue review | 212 chars / 27 words / 2 sentences with every actionable claim preserved |
from slimtoken.prompt_reframe import frame_prompt
tight, system, domain = frame_prompt(user_prompt, mode="balanced")
# → ("Revenue figure for Q3? Lock the plan or reforecast.",
# "Role: generalist. Style: terse. Domain (business): ...",
# "business")Three ways to use it:
# Python API — drop into any script, batch job, or web service
python -c "from slimtoken.prompt_reframe import frame_prompt; \
print(frame_prompt('rambling prompt here')[0])"
# CLI — pipe prompts in, get tight prompts as JSON out
python -m slimtoken.prompt_reframe "your rambling prompt here"
python -m slimtoken.prompt_reframe json "your prompt"
# MCP stdio — for any host that speaks MCP
slimtoken-reframe-mcp
# exposes: slimtoken.reframe.{classify_domain, reframe, shrink,
# minify, build_system, frame}When NOT to use it:
The prompt is already short (< 80 words) — the reframe is a no-op.
You want a semantic paraphrase the input doesn't already contain. Use an LLM for that.
You're a code agent and the prompt is mostly code — never touch code fences.
shrink_prompt has no hidden max_tokens default: mode decides the target
length unless an explicit int is passed.
→ Full algorithm in
skills/prompt-reframe/references/stages.md.
→ Agent Skill manifest: skills/prompt-reframe/SKILL.md.
A realistic bloated session (6 repeated file reads + verbose history, 18 KB body):
$ slimtoken optimize -i request.json
tokens: 4678 -> 1259 (-73.1%)
stages: tools=0 system=True msgs=6 dedup=5 distill=4 budget_drop=0 tool_compressed=1What each stage did to that body:
Stage | Effect on the example |
🔄 dedup | 5 of 6 identical file reads → |
📝 distill | 4 verbose assistant turns → first sentence + |
🗜️ tool_compress | the last file read → |
📋 system | 20 repeated banner lines → 1 |
The model still sees every file's content (in the latest result) and every turn's gist — just not the redundant copies. Measure your own payload:
slimtoken optimize -i request.json --json # full minified body, machine-readable
slimtoken presets --measure # recompute the reduction table on your machine
slimtoken latency # one request through a running proxy → t0-t4 printoutProxy latency is ~12 ms per request (optimize stage, warm) — negligible next to any LLM round-trip. The win is fewer tokens sent, not proxy speed.
Three levers on the streamed response. Filler-strip is on by default — it
removes lead-in filler with no downside. The token cap and stop sequences are
opt-in (they need explicit values). Set SLIMTOKEN_FILLER=0 to disable the
strip; with it off and no cap/stop set, the filter is inert (raw passthrough,
zero overhead).
Lever | Env / flag | Default | What it does |
🧹 filler strip |
| 1 | Drop lead-in filler ("Sure!", "Here is the code:", "Let me know if you need anything else.", …) from the response head. |
✂️ token cap |
| off | Truncate the stream at N output tokens, counted with the real tokenizer. |
🛑 stop sequences |
| off | Cut the stream at the first stop string (not emitted). |
slimtoken serve --upstream http://127.0.0.1:8080 \
--max-tokens 2048 --stop "END" --tool-compress
# or via env (filler needs no flag — it's on):
SLIMTOKEN_MAX_TOKENS=2048 SLIMTOKEN_STOP=END slimtoken serve --upstream http://127.0.0.1:8080The filler strip is a pending-buffer state machine — a phrase split across SSE chunks is still caught:
model emits: "Sure!\nHere is the code:\nprint(1)"
client sees: "print(1)"The token cap and stop truncation are applied to the streamed delta text, so a runaway completion is cut off at the source instead of flooding your context.
Set SLIMTOKEN_STATS_FILE=/path/to/stats.json and the proxy atomically persists
cumulative minify stats after every request (tmp + rename, so a crash never
corrupts the file):
{
"runs": 2,
"tokens_in": 3000,
"tokens_out": 700,
"tokens_saved": 2300,
"ratio_pct": 76.7,
"last_run_ts": "2026-08-11T09:30:00",
"last_saved_pct": 80.0,
"history_60s": [{"ts": "...", "saved_pct": 80.0}, {"ts": "...", "saved_pct": 73.1}]
}GET /metrics on the proxy returns cumulative token counts + the t0–t4 latency
buckets.
There are no named profiles. slimtoken always runs the full pipeline (the old
aggressive preset, minus the name); every stage and knob is a raw SLIMTOKEN_*
env switch. The two things you might actually want to do:
Turn it all off — SLIMTOKEN_MINIFY=0 (raw passthrough; for debugging or
when the model must see input verbatim).
Preserve old user turns — the default already keeps them verbatim; the
lossless pipeline never distills them. Only SLIMTOKEN_DISTILL_INCLUDE_USER=1
opts into compressing them.
Opt into a lossy stage — SLIMTOKEN_TOOL_COMPRESS=1 (type-specific
tool-result reduction) or SLIMTOKEN_MINIFY_DOM=1 (HTML pruning). Both are
OFF by default because they are lossy.
See the Config table for the full knob list. The single config
surface (build_config) is shared by the proxy, CLI, MCP server, and skill.
The proxy routes by URL path and the CLI/MCP accept a --format / format arg.
The minify pipeline is built around Anthropic's request shape; OpenAI and Ollama
bodies are normalized to that canonical form, minified, then converted back — a
thin adapter layer, no optimization logic is duplicated. The Anthropic path is
identity (zero work, byte-identical to before).
path | format | conversion |
|
| none (identity) |
|
|
|
|
| reuses the OpenAI conversion; Ollama-only fields ( |
Pair-safety is preserved across the round trip: an assistant tool call plus its
following role:"tool" replies become Anthropic tool_use + tool_result blocks,
the pipeline drops such pairs together, and the reverse conversion never orphans a
tool result from its call.
# proxy: point any of these at slimtoken; it detects the format from the path
export OPENAI_BASE_URL=http://127.0.0.1:8181/v1 # OpenAI clients → /v1/chat/completions
export OLLAMA_HOST=127.0.0.1:8181 # Ollama clients → /api/chat
slimtoken serve --upstream http://127.0.0.1:11434 # → your local Ollama
# CLI: minify an OpenAI/Ollama body directly
slimtoken optimize -f openai -i req.json
slimtoken optimize -f ollama -i req.jsonRecommended configs for common local models grouped by GPU VRAM tier, each with a
usable context (KV cache + overhead eat into the nominal max). The reduction
column is the live measured token drop the always-on pipeline achieves on the
bloated payload — computed by the pipeline, not hand-waved
(slimtoken presets --measure).
VRAM | model | quant | usable ctx | reduction |
4 GB | Llama 3.2 3B | Q4_K_M | 8 192 | 85.4% |
4 GB | Qwen 2.5 3B | Q4_K_M | 32 768 | 85.4% |
4 GB | Phi-4 Mini | Q4_0 | 16 384 | 85.4% |
8 GB | LFM2.5-8B-A1B (MoE, 1.5B active) | Q4 | 32 768 | 85.4% |
8 GB | Qwen 2.5 7B | Q4_K_M | 32 768 | 85.4% |
8 GB | Gemma 3 12B | Q4 | 16 384 | 85.4% |
16 GB | Qwen 3 14B | Q4_K_M | 65 536 | 85.4% |
16 GB | Mistral Nemo 12B | Q4_K_M | 131 072 | 85.4% |
16 GB | Llama 3.1 8B | Q4_K_M | 131 072 | 85.4% |
Reduction is config-dependent, not model-dependent — the pipeline rewrites the request regardless of which model consumes it, so every tier shows the same number (the always-on config on a bloated payload). On a typical session it's ~34%. Tune the config with the
SLIMTOKEN_*env knobs, not by switching models.
Because slimtoken compresses input ~85%, a model's nominal context window holds
far more raw conversation than its size suggests. The effective capacity is
nominal_ctx / (1 − reduction). The presets below push each tier to the largest
nominal context that fits fully in VRAM (q4_0 KV, flash attention, full GPU
offload, --kv-unified) — computed by the backend optimizer, not asserted — and
show the effective raw-token capacity with compression. Each tier has both a
dense and a MoE/Mamba-hybrid option: hybrids (Qwen3.6-35B-A3B, LFM2.5-8B-A1B)
have ~5 KB/token KV vs ~30 KB/token for dense, so they reach far larger contexts on
the same VRAM.
slimtoken high-context # full table (all tiers, dense + MoE)
slimtoken high-context --vram-gb 16 # one tier
slimtoken high-context --vram-gb 16 --detail # + the llama-server commandstier | kind | model | quant | nominal ctx | total GB | margin | effective ctx |
4 GB | dense | Llama 3.2 3B | Q4_K_M | 16 384 | 3.67 | +0.33 | ~112 k |
4 GB | MoE | LFM2.5-8B-A1B | IQ2_S | 32 768 | 3.91 | +0.09 | ~224 k |
8 GB | MoE | LFM2.5-8B-A1B | Q4_K_M | 131 072 | 7.33 | +0.67 | ~898 k |
8 GB | dense | Llama 3.1 8B | Q4_K_M | 32 768 | 7.71 | +0.29 | ~224 k |
16 GB | MoE | Qwen3.6-35B-A3B | IQ3_S | 131 072 | 14.16 | +1.84 | ~898 k |
16 GB | dense | Llama 3.1 8B | Q4_K_M | 262 144 | 14.71 | +1.29 | ~1.8 M |
The 16 GB MoE row is capped at 128 k — the proven-stable value on a 16 GB card (256 k OOMs at ub=2048; 128 k@ub512 measured 13.7 GB). The 8 GB MoE row is capped at 128 k too (256 k is a razor fit, ~+0.04 GB margin — any VRAM spike spills it; 128 k leaves ~0.67 GB headroom). The 4 GB MoE at 2-bit is a quality trade-off — the dense 3B is usually the better 4 GB pick. All configs use q4_0 KV (
-ctk q4_0 -ctv q4_0) matching a proven local llama-server setup.
On-demand, not the default. The MCP server gives the agent tools it can call when it chooses. It does not minify every request — that's the proxy's job. Use the MCP server when you want the agent to minify on demand (or as a redundancy check that the proxy path is working). For guaranteed every-message minification, use the proxy (see Quick start).
slimtoken-mcp exposes the pipeline as MCP tools over stdio (the transport
every local MCP client uses). It is a thin adapter: every tool imports and calls
an existing core function — no optimization is reimplemented. The proxy and
the MCP server are independent processes that share the same library.
Tool | Calls | What it returns |
|
| minified messages/system/tools + token counts + per-stage stats |
|
| total + per-message token breakdown (cl100k, bundled) |
|
| a ready-to-inject |
|
| a type-compressed tool_result content block (lossy) |
|
| read-only token-budget headroom + would-drop count |
|
| the active MinifyConfig (built from |
|
| VRAM-tier presets, optionally with live measured reduction |
|
| high-context dense+MoE presets per tier, with effective context after compression |
optimize_messages, estimate_tokens, and inspect_budget accept a format
field (anthropic / openai / ollama); non-anthropic bodies are normalized to
canonical before the pipeline runs and returned in the caller's format.
Wire it into an MCP client's stdio config (example for Claude Desktop /
claude_desktop_config.json):
{
"mcpServers": {
"slimtoken": {
"command": "slimtoken-mcp"
}
}
}The server speaks the MCP JSON-RPC 2.0 protocol (initialize → tools/list →
tools/call), protocol version 2024-11-05, and is self-contained (stdlib only on
top of slimtoken's existing deps). It does no optimization itself — every call
dispatches to the core pipeline.
Client | MCP registration | Skill install |
Claude Code | Add to |
|
Codex |
|
|
OpenCode |
|
|
Verify with claude mcp list, codex mcp list, or opencode mcp list — the
server should show as connected. The skill directory is the same
skills/slimtoken-optimizer/ folder in every case; copy it into the client's
skill search path (see Agent Skill).
Proxy-first. The skill tells the agent that slimtoken runs as a proxy by default and every request is minified automatically — so the agent doesn't need to call anything. The CLI/MCP tools in the skill are the fallback for when the proxy isn't in the path (on-demand minification).
The skills/slimtoken-optimizer/ directory is a packaged Agent Skill (static
files a host agent runtime — Claude Code, ADK, Gemini CLI, Cursor — reads from
disk on activation, not a running process). It is model-agnostic and works
with local, cloud, or uncensored models: it rewrites the request, not the model.
skills/slimtoken-optimizer/
SKILL.md # L1 description (~50 tok) + L2 body (<800 tok)
references/optimization-policies.md # full stage list + pair-safety rules (loaded on demand)
scripts/optimize.py # wrapper: CLI primary, MCP stdio fallbackThe wrapper shells out to the slimtoken CLI when available, and falls back to a
one-shot MCP stdio call (slimtoken-mcp) when only the MCP server is installed —
so the skill works regardless of which surface the host has:
python3 skills/slimtoken-optimizer/scripts/optimize.py optimize -i req.json
python3 skills/slimtoken-optimizer/scripts/optimize.py presets --vram-gb 16 --measure
python3 skills/slimtoken-optimizer/scripts/optimize.py estimate -i req.jsonDrop the skills/slimtoken-optimizer/ directory into your agent's skill search
path and the host runtime surfaces it when a prompt matches "shrink / minimize /
trim tokens / context too long".
Defaults are the recommended values. Set any to 0 to disable.
Env var | Default | Meaning |
| 1 | master switch; 0 = passthrough |
| 1 | |
| 1 | |
| 1 | |
| 1 | |
| 1 | |
| 131072 | 0 disables hard prune (distill still runs) |
| 4 | recent turns kept verbatim by distill/budget |
| 200 | only dedup tool results at least this long |
| 160 | max chars per distilled old turn |
| 0 | 1 = also distill old user turns (default keeps them verbatim) |
| (none) | comma-list of tool names to never minify |
| 0 | lossy type-specific tool-result compression (opt-in) |
| 0 | lossy opt-in: prune large HTML tool_results |
| (unset) | output-token cap (enables output filter) |
| (unset) | comma-joined stop sequences (enables output filter) |
| 1 | strip lead-in filler ("Sure!", "Here is the code:") from the response head; 0 = off |
| (unset) | path to persist cumulative minify stats (runs, tokens in/out, saved %, 60-run history) as JSON |
| 0 | use HTTP/2 to the upstream |
| 8181 | listen port |
| (required to serve) | backend URL |
TLS for cloud HTTPS upstreams is handled by httpx (SNI; optional mTLS via
SLIMTOKEN_TLS_*; SLIMTOKEN_TLS_INSECURE=1 to skip verify). Lazy MCP — one
stub tool per configured MCP server in ~/.slimtoken/lazy_mcp.json, the real
server spawned on call — is available as a separate entrypoint.
flowchart TB
subgraph Fit[fit the model in VRAM]
W[weights<br/>-ngl 999 full offload] --> K[KV cache<br/>-ctk/-ctv q4_0]
K --> CB[compute buffer<br/>--kv-unified]
end
subgraph Speed[decode speed levers]
FA[flash attention<br/>-fa on] --> UB[big ubatch/batch<br/>-ub N -b N]
end
C[context window<br/>-c N] --> Fit
Speed --> Result[2-4× decode speedup<br/>~50-75% less wall-clock<br/>~2× context capacity]
Fit --> Resultslimtoken config-optimizer [--model /path/to.gguf] [--vram-gb 16] [--model-size-gb 12.7]
[--kv-per-token 5120] [--native-ctx 262144]config-optimizer inspects your GPU VRAM and model size, estimates weights
VRAM, KV cache, and the compute buffer, then recommends llama-server arguments
that fit without OOMing. It prints a ready-to-paste llama-server command plus
CORTEXAGENT_* env exports. It changes nothing itself — recommend-only.
Option | Flag | What it does | Potential gain |
🟢 Full GPU offload |
| All model layers on GPU. Decode is memory-bandwidth-bound — offloading even a few layers to CPU cripples speed. | The biggest decode lever; often several× vs partial offload. |
⚡ Flash attention |
| Fused attention kernel; lower VRAM, faster attention. | Largest on long context (up to ~2× on the attention portion). |
🗜️ KV cache quant |
| Halves KV cache size. | ~2× context capacity in the same VRAM; modest decode speedup. |
📏 Context window |
| Largest ctx that fits without OOM. | More usable history (capacity, not speed). |
📦 Ubatch / batch |
| Larger prompt-eval batch. | Faster input processing — compounds with slimtoken's input reduction. |
🔗 KV unified |
| Unified compute buffer (calibrated into the VRAM estimate). | Lower buffer overhead. |
🔀 Parallel slots |
| 1 slot = max per-request budget (raise for concurrency). | Higher throughput under concurrent load. |
Total potential: versus a naive baseline (partial CPU offload + fp16 KV +
no flash attention), enabling all of the above typically yields a 2–4× decode
speedup (≈50–75% less wall-clock per token) and ~2× context capacity. These
are typical llama.cpp ranges, not measurements taken by slimtoken — the real
figure depends on your starting config. config-optimizer computes the largest
safe values for your specific VRAM automatically.
⚠️ Estimate only — verify VRAM with
nvidia-smiunder a real prompt before trusting the margin. The compute buffer is calibrated for--kv-unifiedon a hybrid MoE; dense models or--kv-budgetchange the math.
python3 -m pytest tests/ -q # 32 checks — core pipeline + proxy + adapters + context presetsCover fence byte-identity, pair-safety, dedup, distill, ≥50% default reduction
on a bloated payload, real-tokenizer counting (no whole-body serialize),
single-pass equivalence, type-compressor pair-safety, output-filter truncation +
filler-strip, DOM pruning, stats persistence, async proxy end-to-end, /metrics
latency buckets, fast-path byte-identical passthrough, and the full MCP stdio
handshake + every tool + the error paths.
MIT, Copyright (c) 2026 greyok00. See LICENSE.
Count tokens in a request body using the real cl100k_base tokenizer (bundled, offline). Returns total + per-message breakdown. The model arg is accepted for forward-compat but the count is cl100k-approximate for non-cl100k models.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | model name (informational only) | |
| tools | No | ||
| format | No | request format of the body (normalized to canonical before counting) | anthropic |
| system | No | ||
| messages | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behavior: bundled/offline tokenizer, exact for cl100k, approximate for non-cl100k models. Without annotations, this carries the transparency burden; it also describes return shape (total + breakdown).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the main function. Each sentence adds value—tokenizer type, return output, model parameter caveat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers the core operation, offline behavior, approximation caveat, and return structure. Lacks elaboration on the `tools`/`system` parameters, but the 'request body' phrasing implies they're included.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema only documents model and format (40% coverage), and the description adds meaning for `model` (informational, approximate for non-cl100k). But it does not clarify `messages`, `tools`, `system`, or `format` usage beyond the schema's minimal description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb-object structure: 'Count tokens in a request body'. The addition of 'real cl100k_base tokenizer' and 'total + per-message breakdown' gives specific scope and differentiates from sibling tools like optimize or prune.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for counting tokens, but doesn't explicitly state when to prefer this over sibling tools like inspect_budget or optimize_messages. No exclusions or alternative guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the slimtoken config in use: the always-on MinifyConfig built from SLIMTOKEN_* env knobs (the single config surface shared by the proxy, CLI, and MCP server). Useful to see what slimtoken will do.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses the config is 'always-on' and 'shared by the proxy, CLI, and MCP server,' indicating a persistent, global read-only resource. The word 'Return' implies no side effects, but it does not explicitly state that or describe error behavior, which is a minor gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action, and each clause adds value. It efficiently explains what the tool does, why it exists, and how it's configured without extraneous detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description provides enough context: it defines the returned object (MinifyConfig), its construction, and its shared role. This is complete for a simple config retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds context about the config's origin and scope, which is meaningful even though there are no parameters to document. It avoids redundancy with the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Return the slimtoken config in use' with a specific verb and resource. It further distinguishes the tool from siblings by specifying the config is built from SLIMTOKEN_* env knobs and shared across proxy, CLI, and MCP server, which is distinct from the optimization/estimation/pruning tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear usage context: 'Useful to see what slimtoken will do.' This implies when an agent should call it, but it does not explicitly mention alternatives or when not to use it. Since the siblings are functionally distinct, the context is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
High-context VRAM-tier configs (dense AND MoE) showing how slimtoken compression expands the effective context window. Each row gives the largest nominal context that fits fully in VRAM (computed by config_optimizer, q4_0 KV, flash attn, full offload) and the effective raw-token capacity = nominal_ctx / (1 - reduction). Use best=true for just the largest-effective-context preset of a tier.
| Name | Required | Description | Default |
|---|---|---|---|
| best | No | return only the largest-effective-context preset for the tier | |
| vram_gb | No | filter to one tier (4/8/16) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does a good job: it discloses the computation assumptions (config_optimizer, q4_0 KV, flash attn, full offload) and provides the formula for effective raw-token capacity. It also clarifies that rows represent presets and that best=true filters to the largest. Minor omissions include not stating default behavior when vram_gb is omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the primary purpose, then details the computation, the formula, and a useful parameter tip. Every sentence adds value and there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the moderate complexity (VRAM tiers, compression, formulas), the description provides sufficient context for an agent to understand what the tool returns and how the values are computed. The lack of an output schema is partially mitigated by the description's mention of 'rows' and the formula. The main gap is not specifying the full return set when vram_gb is omitted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds no new parameter-specific syntax beyond the schema, but it does provide context for interpreting the output (formula for effective capacity). This is helpful but not essential, given the schema already explains both parameters clearly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as providing high-context VRAM-tier configs for both dense and MoE models, with a specific scope of showing how compression expands context. It distinguishes itself from siblings like list_model_presets by focusing on VRAM tiers and effective context capacity. However, it lacks an explicit verb like 'list' or 'get', which slightly reduces clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a specific usage hint for the 'best' parameter ('Use best=true...'), which is helpful. However, it does not explicitly state when to use this tool over alternatives like list_model_presets or get_config. The context is implied by the title and description, but no exclusions or alternative comparisons are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read-only token-budget inspection: counts system/tools/messages, reports headroom against a token_budget, and whether the pair-safe pruner would drop any leading messages. Does not modify the body.
| Name | Required | Description | Default |
|---|---|---|---|
| tools | No | ||
| format | No | request format of the body (normalized to canonical before inspection) | anthropic |
| system | No | ||
| messages | Yes | ||
| keep_last | No | ||
| token_budget | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description shoulders the full burden of behavioral disclosure. It explicitly states the tool is read-only, does not modify the body, and details what it counts and reports. This is solid transparency for an inspection tool, though it omits specifics like return format or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with 'Read-only token-budget inspection', and every phrase adds value. It avoids redundancy and clearly communicates the tool's core behavior without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations and output schema, the description covers the essential operational semantics: what it inspects, what it reports, and its side-effect-free nature. It does not elaborate on input format normalization or parameter details, but for a read-only inspection tool, the provided context is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has only 17% description coverage, so the description must compensate. It meaningfully explains the role of 'messages', 'system', 'tools', and 'token_budget' through the counting and headroom reporting, but it does not clarify 'format' (beyond schema enum) or 'keep_last', leaving gaps for those parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('inspect') and resource ('token-budget'), and enumerates the exact outputs (counts, headroom, pruner decision). It distinguishes itself from sibling tools by emphasizing its read-only inspection nature, contrasting with the more action-oriented siblings like optimize_messages and prune_context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through 'Read-only' and 'Does not modify the body', signaling that this is for inspection, not modification. However, it does not explicitly name alternative tools or provide when-to-use vs. when-not-to-use guidance, so it stops short of the highest score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List recommended local-model presets by GPU VRAM tier (4/8/16GB), each with a usable context. With measure=true, enriches each row with the live measured token reduction on a bloated payload (run by the pipeline itself).
| Name | Required | Description | Default |
|---|---|---|---|
| measure | No | run the pipeline to measure real reduction | |
| vram_gb | No | filter to one tier (4/8/16) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that 'List' is a read-only action and explains that measure=true triggers the pipeline to measure live token reduction, implying additional processing. It does not mention auth or rate limits, but for a listing tool this is reasonable context. The description adds valuable behavioral detail beyond the bare schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two sentences that are tightly packed with relevant information. The main purpose is front-loaded, and the second sentence adds a conditional behavior. There is no fluff or redundancy; every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with two optional parameters and no output schema, the description provides sufficient context: what the tool lists, the filtering criterion, and the optional enrichment behavior. It mentions that each preset 'has a usable context', giving a hint of the return content. It could be slightly more explicit about the exact response structure, but it does not leave a major gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds meaning to the 'measure' parameter by explaining it 'enriches each row with the live measured token reduction on a bloated payload (run by the pipeline itself)', which is not in the schema. It also confirms the VRAM tiers (4/8/16GB) that align with the vram_gb parameter, providing useful context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'List recommended local-model presets by GPU VRAM tier (4/8/16GB)', which is a specific verb+resource+scope statement. It clearly distinguishes this tool from sibling tools like optimize_messages, prune_context, and minify_tool_result, which are mutation/optimization tools, whereas this is a read-only listing operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use this tool: when the user needs recommended local-model presets filtered by GPU VRAM tier. It also explains the optional measure=true behavior. However, it does not explicitly contrast with the sibling 'high_context_presets' tool or provide exclusions, so it stops short of full alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compress a large tool_result content block using type detection (directory listing, git output, logs, JSON, source). LOSSY — emits a compact representation plus a [slimtoken-compressed] metadata header. Pair-safe by construction (only the content field changes).
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | tool_result content (string or list of text blocks) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and transparently discloses that the operation is LOSSY, emits a metadata header, and is pair-safe by construction (only the content field changes). This goes beyond a simple action verb and informs the agent about side effects and limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action, and every clause earns its place. It conveys purpose, behavioral traits, and safety in a compact form without repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter, no output schema, and no annotations, the description covers the core required context: it states what is compressed, how (type detection), the lossy nature, the output metadata marker, and the pair-safe guarantee. It does not specify exact output structure but sufficiently sets expectations for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the parameter is already well-documented. The description adds minimal extra meaning beyond noting 'large' content and type detection, which does not change the parameter syntax or semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb 'Compress' and resource 'tool_result content block', and lists concrete type detection categories (directory listing, git output, logs, JSON, source). This clearly differentiates it from sibling tools like optimize_messages or estimate_tokens.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for large tool_result content blocks but does not explicitly state when to use this tool over alternatives or when not to use it. There is no reference to sibling tools or exclusions, so it stays at the 'implied usage' level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Reduce prompt size while preserving message structure and tool-call validity (pair-safe, fence-aware). Returns the minified messages plus token counts. Lossy by default (distill + tool-result compression); disable stages via the SLIMTOKEN_MINIFY_* env knobs.
| Name | Required | Description | Default |
|---|---|---|---|
| tools | No | tool definitions to minify | |
| format | No | request format (anthropic=identity; openai/ollama are normalized to canonical, minified, then returned) | anthropic |
| system | No | system prompt (string or list of text blocks) | |
| messages | Yes | Anthropic-style messages array | |
| max_input_tokens | No | override the token_budget (hard prune cap) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It explicitly states 'Lossy by default (distill + tool-result compression)' and mentions how to disable stages via SLIMTOKEN_MINIFY_* env knobs, which is meaningful for a mutation-like tool. It could go further by explaining 'distill' or authentication needs, but the disclosure is strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the core purpose in the first sentence and key behavioral caveats in the second. Every word earns its place, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description mentions the return value (minified messages plus token counts) and the lossy default with env-knob control, which is sufficient for a tool with a well-documented schema. However, it does not elaborate on what 'pair-safe, fence-aware' entails or how it differs from sibling tools, leaving minor gaps for deeper contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and each parameter already has a descriptive schema entry, including format behavior and default. The description adds no additional parameter-level detail beyond what the schema provides, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource: 'Reduce prompt size while preserving message structure and tool-call validity (pair-safe, fence-aware).' This clearly states the operation and its distinctive scope, distinguishing it from sibling tools like prune_context by emphasizing lossy compression and structural preservation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use the tool (for reducing prompt size while preserving structure) and gives config context via env knobs. However, it does not explicitly mention when not to use it or name alternatives among siblings, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
RAG-style context pruning for a memory/conversation store: strip low-value text, retrieve warm entries relevant to a query, sliding-window summarize old turns, and enforce a token budget. Returns a ready-to-inject / prompt block.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | current query for relevance retrieval | |
| cold_data | No | cold memory keyed by category (each value is a list of entries) | |
| max_tokens | No | ||
| warm_entries | Yes | warm/conversation entries (role+content dicts) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It outlines the internal algorithm (strip, retrieve, summarize, enforce) and the return type, but it does not disclose whether the operation is read-only, whether it modifies the underlying store, or any side effects such as data loss. This ambiguity prevents a higher score.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary purpose, and includes only essential details. Every phrase contributes to understanding the tool's behavior and output, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the return value (a prompt block with cold_memory/recent_context), which is essential since there is no output schema. It also covers all key operations and the overall context of use. However, it does not specify the exact structure of cold_data beyond the schema, nor does it mention the required parameter warm_entries, though the schema handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes 3 of 4 parameters (75% coverage), and the description adds marginal context by linking 'query' to relevance retrieval and 'warm entries' to sliding-window summarization. However, it does not add significant new meaning or format details beyond what the schema provides, especially for cold_data and max_tokens.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'prunes context' in a RAG-style manner, detailing specific operations: stripping low-value text, retrieving warm entries, summarizing old turns, and enforcing a token budget. This distinguishes it from sibling tools like optimize_messages or estimate_tokens, which focus on different aspects of token management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for context pruning in memory/conversation stores but does not explicitly state when to use this over alternatives. No comparisons are made to sibling tools like optimize_messages or minify_tool_result, so the guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.3.3slimtoken.estimate_tokensslimtoken.get_configslimtoken.high_context_presetsslimtoken.inspect_budgetslimtoken.list_model_presetsslimtoken.minify_tool_resultslimtoken.optimize_messagesslimtoken.prune_contextSeveral tools overlap in purpose: optimize_messages, prune_context, and minify_tool_result all reduce token counts, while list_model_presets and high_context_presets both list presets. However, descriptions clarify distinct use cases (full prompt vs. memory store vs. tool result; standard vs. high-context configs), so most boundaries are navigable.
Most tools follow a clear verb_noun pattern (optimize_messages, estimate_tokens, prune_context, minify_tool_result, inspect_budget, get_config, list_model_presets), but high_context_presets breaks the pattern as an adjective_noun phrase, causing a minor inconsistency.
With 8 tools, the set is well-scoped for a specialized token optimization server. Each tool addresses a distinct aspect (estimation, minification, pruning, budget inspection, config, presets) without unnecessary bloat or redundancy.
The tool surface covers the core lifecycle of token optimization: estimating, minifying, pruning, inspecting budgets, and retrieving config/presets. Minor gaps exist (e.g., no explicit tool to reverse or restore compressed content), but these are not essential given the lossy and config-driven design.
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MCP server for progressive tool usage at any scale (see https://klavis.ai)
Hosted MCP server for LLM cost estimation, model comparison, and budget-aware routing.
MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.
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