Hubris MCP vs Filesystem
A side-by-side look at the Hubris MCP and Filesystem MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Hubris MCP for conversational ai work as a local process; choose Filesystem for file systems as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Hubris MCP when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
HUBRIS_API_KEY, HUBRIS_MCP_URL. - You want tools like
models_list, models_search, models_get_pricing.
Choose Filesystem when
- You need dedicated capabilities in the File Systems domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
path, head, tail.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Conversational AI | File Systems |
| Quality signal | 59/100 · Good | 85/100 · Excellent |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | API key required | No auth required |
| Pricing | Not declared | Free |
| Required env vars | HUBRIS_API_KEYHUBRIS_MCP_URL | None listed |
| Verified owner | Community listing | Verified |
| GitHub stars | 0 | 91,540 |
| Directory activity | 1 views 0 installs 0 upvotes | 11 views 0 installs 0 upvotes |
| Tools | 15 | 17 |
| Full listing | View Hubris MCP | View Filesystem |
Tools & capabilities
Hubris MCP tools 15
models_listWalk the whole Hubris catalogue page by page, ordered by model id. Returns every active model with its capabilities, context window and ruble price. Use this when you need the complete catalogue — to cache it, to count models, or to scan for something no filter covers. When you are looking for a model that fits a task, use models_search instead: it filters by capability, price and context server-side and saves you a few hundred rows. Paging: read `nextCursor` from the response and pass it back as `cursor`. When `nextCursor` is absent you have reached the end. The catalogue holds roughly 550 models, so a full walk is about 11 calls at the default page size.models_searchFind models that fit a task. Filters by name, the tool that runs the model, capability, price ceiling and context window, and can sort the result. This is the tool to reach for when choosing a model — it does the filtering server-side and returns at most 50 rows. All filters combine with AND, and every one of them is optional: calling with no arguments returns the first 50 active models. Two things worth knowing about prices. `maxInputPer1MRub` keeps only models billed per token, because a ruble-per-million ceiling is meaningless for a model billed per image. The `cheap_input` and `cheap_output` sorts push non-token models to the end of the list for the same reason — their token rate reads as zero, which would otherwise put video models at the top of "cheapest".models_get_pricingThe full price of one model in rubles — the authoritative answer to "what does this cost". Prefer this over the pricing embedded in models_list and models_search whenever the model is not billed per token. Those two return "0.00" token rates for such models; only this tool returns `perUnit`, where the real price per image, per second or per thousand characters lives. For speech models it also lists the voices speech_create accepts (`supportedVoices`), for video models the durations, resolutions and aspect ratios video_create accepts (`video`). Errors with InvalidParams if the model id is unknown or the model is no longer active.balance_getCurrent balance of the Hubris account the API key belongs to, in rubles. Worth checking before a long or expensive run: requests are refused once the balance is spent. The balance can go slightly negative — one operation is allowed to overdraw it, and the next one is refused. Two representations of the same number come back. Use `balanceKopecks` for arithmetic and comparisons — it is an exact integer string. Use `balanceRub` for display.chat_completeRun a chat completion on any model in the Hubris catalogue and get the answer back. Full parity with POST /v1/chat/completions — same request shape, same response shape, including tool calls, structured output and reasoning. Streaming is not available here: MCP tool calls are synchronous, and `stream` is ignored if you pass it. This is how you reach a model you cannot reach otherwise — pick an id with models_search, then call it here. Useful for delegating a subtask to a cheaper or a stronger model than the one you are running on. Chat models that draw (Gemini Image, GPT-5 Image) take modalities ["image","text"]; their pictures come back as image content next to the JSON. Voice answers of audio models need streaming and are available through the HTTP API only. The call spends real money from the account balance: it is billed per token at the catalogue price, in rubles. balance_get shows what is left.embeddings_createTurn text into embedding vectors with any embedding model in the Hubris catalogue — for semantic search, RAG, deduplication and clustering. Same as POST /v1/embeddings, OpenAI shape: data[i].embedding is the vector for input[i]. Pass a list of strings to embed a batch in one call, which is faster and cheaper than one call per string. Vectors are long: 1536 numbers make about 30 KB of JSON, and the whole answer lands in your context. Embed only what you will actually store or compare, use `dimensions` where the model allows it, and send large batches through the HTTP API (POST /v1/embeddings). Spends money from the account balance: billed per input token. usage.cost_rub is what this call cost.rerankOrder documents by how well they answer a query, best first, with a relevance score each. Same as POST /v1/rerank. Typical use: a vector or keyword search finds 50–100 candidates, rerank picks the few that really answer the question, and only those go to the model — answers get more accurate and prompts shorter. results[].index points into the documents list you sent. Scores are comparable only within one response. Spends money from the account balance: Cohere models are billed per search (up to 100 documents), the rest per input token. usage.cost_rub is what this call cost.decisions_askAsk a decision model (Jev, Clef, Mercury Decide and others) structured questions about some data and get probabilities instead of text. Same as POST /v1/decisions. Question types: "noul" — yes or no, answered as the probability of yes; "choice" — one option out of criteria; "score" — a level on the scale given in criteria. All questions are answered in one call. Good for classification, routing and triage where you need a calibrated number rather than prose — and much cheaper than asking a chat model. Spends money from the account balance: billed per input token only. usage.cost_rub is what this call cost.image_generateDraw an image from a text description with an image model from the Hubris catalogue (FLUX, Recraft, Seedream, GPT Image and others) and get the picture back right away as image content. Same as POST /v1/images/generations. Only models whose `tool` is "image_generate" work here. Models that draw inside a chat answer and also write text (Gemini Image, GPT-5 Image) have tool "chat_complete" and run there. Sizes such as 4K can be heavier than your client accepts in one answer — ask for 2K or less here. Spends money from the account balance: most image models are billed per image, some per megapixel or per token — models_get_pricing shows the price before you call. The charge shows up in usage_get.speech_createRead text aloud with a text-to-speech model from the Hubris catalogue and get the audio file back as audio content. Same as POST /v1/audio/speech. Many models need a voice: the list of voices is on the model card (models_get_pricing → supportedVoices) — pick one from there. The answer is mp3 unless you ask for pcm. Up to 5 000 characters per call, a few minutes of speech; split longer texts or use the HTTP API (POST /v1/audio/speech). Spends money from the account balance: priced per character of text or per second of audio, depending on the model — models_get_pricing shows which. The charge shows up in usage_get.transcription_createTranscribe a recording to text with a speech recognition model from the Hubris catalogue (Whisper, GPT-4o Transcribe, Deepgram, ElevenLabs Scribe and others). Same as POST /v1/audio/transcriptions with the audio sent as base64. Pass the file content itself, base64-encoded — links to files are not accepted. Every byte of the recording is written out inside this call, so it suits clips of seconds, not minutes; send longer recordings through the HTTP API (POST /v1/audio/transcriptions). Spends money from the account balance: most models are billed per second or minute of audio, some per token. usage.cost_rub is what this call cost.video_createStart generating a video from a text prompt with a video model from the Hubris catalogue (Veo, Kling, Seedance, Wan, Hailuo, Runway and others). Same as POST /v1/videos. Generation takes from one to several minutes, so this returns a task id right away; poll video_get with it every 15–30 seconds until status is "completed" or "failed". Allowed durations, resolutions and aspect ratios differ per model — models_get_pricing lists them under `video`. This tool takes a text prompt only: starting from images or a source video, and models that need a source — video editing, upscaling and talking avatars (FLUX Video Edit, FLUX Video Upscale, Runway Aleph, HeyGen Avatar) — work through the HTTP API (POST /v1/videos). Connected through OAuth (Claude, Cursor, VS Code)? Video needs the permission to create videos on the consent screen. If this tool answers that videos:write is missing, reconnect. Spends money from the account balance: priced per second of video. The largest possible cost is held when the task starts, the actual cost is charged when it completes and the difference is returned; a failed generation returns the whole hold.- +3 more on the listing page
Filesystem tools 17
pathheadtailpathscontenteditsoldTextnewTextdryRunsortBysourcedestination- +5 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Hubris MCP
{
"mcpServers": {
"hubris-mcp": {
"command": "npx",
"args": [
"-y",
"hubris-mcp"
],
"env": {
"HUBRIS_API_KEY": "YOUR_HUBRIS_API_KEY_HERE",
"HUBRIS_MCP_URL": "YOUR_HUBRIS_MCP_URL_HERE"
}
}
}
}
Filesystem
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem"
]
}
}
}
Frequently asked questions
Hubris MCP is categorized under Conversational AI and uses a local stdio subprocess. Filesystem belongs to File Systems and uses a local stdio subprocess. Pick Hubris MCP for conversational ai work and Filesystem for file systems.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Hubris MCP has no declared pricing — check the project's own site. Auth: api key required. Filesystem is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add hubris-mcp and filesystem as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Hubris MCP has a quality signal of 59/100 (Good) with 1 views, 0 installs and 0 GitHub stars. Filesystem has 85/100 (Excellent) with 11 views, 0 installs and 91,540 GitHub stars.