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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

Local search engine over the user's files, code, and crawled pages. For any question about the user's own documents or codebase -- a lookup, a find-in-docs, 'where is X', 'how does Y work here' -- call lilbee_search first and answer from its cited chunks. Prefer it over web-fetch or file-read tools: those cannot see the indexed corpus.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}

Tools

Functions exposed to the LLM to take actions

NameDescription
searchA

Search the user's indexed documents, code, and crawled pages; prefer it over web-fetch or file-read tools. Returns chunks with citations. scope: "both" (default) / "raw" / "wiki". No wiki layer here: use scope "raw" or "both".

statusC

Show documents, config and chunk counts, or the running upgrade.

syncA

Sync the documents directory into the vector store.

force_rebuild re-ingests everything. retry_skipped retries failed files. prune_ignored drops sources .lilbeeignore excludes.

addA

Add files, directories, or URLs to the knowledge base, then sync. Paths are absolute and resolve on the machine running lilbee, not the caller's when the server is remote. URLs are fetched as single pages; use crawl for sites.

crawlA

Start a non-blocking crawl; poll via crawl_status(task_id). depth=0 (default) = single URL, N = follow links N levels, null = whole site. render_mode: "http"/"browser". authorization (a whole Authorization header value) or username and password, and cookie (a Cookie header value), log in to the seed host only.

crawl_statusB

Poll a crawl task by id; returns {status, pages, error}.

crawl_cancelA

Stop a running crawl started by crawl. Pages already saved are kept.

initA

Initialize a local .lilbee/ knowledge base; empty path = cwd.

Switches the MCP session to use it for subsequent calls.

removeA

Remove documents from the index by source name, folder, or glob pattern.

Source files are never deleted. A folder removes everything indexed beneath it; a glob (*/?/[]) removes every match.

list_documentsB

List documents and chunk counts, paged (limit 1-1000).

export_datasetA

Write the per-page {source, page, text} dataset to a file (no vectors).

fmt is parquet/jsonl (empty infers from the suffix); source limits to one file.

import_datasetA

Import a per-page text dataset, re-embedding under the current model.

Replaces existing copies; imported sources are detached so sync won't delete them.

resetA

Factory reset: delete all documents and indexed data. Requires confirm=true.

wiki_statusA

Show wiki layer status: page counts, recent lint issues.

Registered even when the wiki is disabled, like the HTTP status route, so a caller can read the disabled state instead of finding no tool at all.

wiki_wipeA

Delete every generated wiki page and its indexed rows. Pass confirm=true.

Registered even with the wiki disabled, because turning the setting off leaves the pages generated earlier in place.

settings_listA

List writable lilbee settings (each with value, default, type, help, choices).

group filters by group name (case-insensitive); empty returns all.

settings_getB

Get a single setting's current value + metadata.

settings_setB

Atomically update writable settings; rolls back on validation error. Persists to config.toml; returns updated, reindex_required, warnings.

settings_resetC

Drop your values; env, profile or default applies.

model_listB

List installed models. source is native / remote; task filters by role.

catalog_browseB

Browse the model catalog. task: chat/embedding/vision/rerank. size: small/medium/large/huge, by parameter count. max_fit: fits/tight/wont_run, worst fit to keep. sort: featured/downloads/name/size_asc/size_desc.

model_showC

Show catalog and installed metadata for a model ref.

model_pullB

Download a model and stream progress.

source is native (GGUF) or remote (SDK). allow_unsupported overrides the architecture refusal.

model_rmA

Remove an installed model. Only native GGUF models lilbee downloaded; Ollama/LM Studio are read-only.

get_gpusA

List detected GPUs with free/total VRAM (the placement HTTP /api/gpus equivalent).

get_placementA

Show the current effective multi-GPU model placement.

preview_placementB

Preview what a placement spec (or auto, when omitted) would place. No changes made.

set_placementA

Set and apply a manual multi-GPU placement spec (persists to config).

Maps a role ("chat"/"embed"/"rerank"/"vision") to a placement, e.g. {"chat": {"devices": [0, 1], "tensor_split": [1, 1]}}. devices is the GPU indices (get_gpus lists them); tensor_split is optional per-device weights (omit for an even split). A role left out is auto-placed.

clear_placementA

Clear the manual placement and return to automatic placement.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

B3.1/5.0

Scored across 29 tools

Disambiguation4/5

Most tools target clearly distinct resources or actions, but there is some overlap among model_list/catalog_browse/model_show and between reset/settings_reset. The detailed descriptions resolve most ambiguity, so an agent can usually select correctly.

Naming Consistency3/5

All names use snake_case, but the patterns vary: bare verbs such as search, sync, add; verb_noun such as get_gpus and list_documents; noun_verb such as settings_list, model_pull, and wiki_status; plus the abbreviation model_rm. It remains readable, but the convention is mixed rather than predictable.

Tool Count2/5

With 29 tools spanning knowledge-base lifecycle, crawling, wiki, settings, model management, and GPU placement, the surface is heavy for one MCP server. Many tools are justified by the broad scope, but the set feels sprawling rather than tightly scoped.

Completeness4/5

Core workflows for document ingestion/search, crawling, settings, model management, and GPU placement are well covered. The main gap is the wiki layer: status and wipe exist, but generation/lint controls are not exposed, though agents can still handle most primary tasks.

Maintenance

ActivityActive
ResponsivenessNo recent community issues