This MCP server lets you administer AnythingLLM workspaces and keep their vector stores in sync with local Markdown documentation via an ETL daemon.
list_workspaces: List all AnythingLLM workspaces with slugs, IDs, and vector counts (read-only).
create_workspace: Create a new isolated workspace with custom settings.
delete_workspace: Permanently delete a workspace and purge its vector storage (destructive).
get_system_env: Retrieve system environment, vector DB provider, and storage diagnostics.
get_vector_count: Get vector counts system-wide or for a specific workspace slug.
anythingllm-sync daemon: Scan project directories for Markdown files, deduplicate via SHA-256, upload new/modified docs to matching workspaces, and remove vector entries when files are deleted locally.
Operational flexibility: Run as a stdio MCP server, configure via environment variables or CLI flags, run one-shot syncs or continuous intervals.
Click on "Deploy 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., "@AnythingLLM Control Plane MCP Server List all my workspaces."
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.
High-performance Go-based Model Context Protocol (MCP) server and automated ETL sync daemon for AnythingLLM instances (TheNovaNodes/anythingllm-mcp-control). Provides autonomous AI agents with administrative workspace management and keeps semantic vector stores in continuous synchronization with filesystem Markdown documentation.
Related MCP server: anythingllm-mcp
The project builds two specialized Go binaries:
anythingllm-control — Stdio-based MCP server exposing an administrative interface for managing AnythingLLM workspaces, vector indices, and system diagnostics.
anythingllm-sync — Autonomous ETL synchronization CLI and daemon that monitors local project directories, deduplicates content via SHA-256, and handles tombstone lifecycle tracking in pure Go SQLite.
The anythingllm-control MCP server exposes 5 administrative tools:
list_workspaces
Lists all active workspaces in AnythingLLM with their slugs, IDs, and vector counts.
Arguments: None.
Side Effects: None (read-only).
create_workspace
Creates a new isolated workspace with custom settings and vector indexing.
Arguments: name (string, required).
Side Effects: Creates a new workspace in the AnythingLLM database.
delete_workspace
Permanently removes a workspace and purges its associated vector storage.
Arguments: slug (string, required).
Side Effects: Destructive permanent deletion of the workspace.
get_system_env
Retrieves system environment settings, vector DB provider, and storage diagnostics dump.
Arguments: None.
Side Effects: None (read-only).
get_vector_count
Retrieves total vector counts system-wide or for a designated workspace slug.
Arguments: slug (string, optional).
Side Effects: None (read-only).
Go 1.25 or higher
GCC or Clang (optional, pure Go SQLite is used)
git clone https://github.com/TheNovaNodes/anythingllm-mcp-control.git
cd anythingllm-mcp-control
make buildCompiled binaries will be created in ./bin/:
bin/anythingllm-control
bin/anythingllm-sync
sudo cp bin/anythingllm-control /usr/local/bin/
sudo cp bin/anythingllm-sync /usr/local/bin/anythingllm-control < /dev/nullThe server will log its initialization status and exit cleanly upon receiving EOF.
The server and sync daemon read configuration from environment variables or command-line flags:
ANYTHINGLLM_BASE_URL (or ALM_BASE)
Base URL of the AnythingLLM REST API.
Default: http://127.0.0.1:3002/api/v1
ANYTHINGLLM_API_KEY (or MG_API_KEY)
Bearer API key for AnythingLLM authentication.
anythingllm-sync)anythingllm-sync scans repositories for Markdown documentation, calculates SHA-256 checksums, and uploads new or modified documents into matching AnythingLLM workspaces. When a tracked file is deleted locally, it records a tombstone and removes the document from the vector index.
-projects (string)
Root directory containing projects or agent offices to scan.
Default: /root/projects
-state-dir (string)
Directory storing the local SQLite state ledger (sync_state.db).
Default: /root/projects/TheNovaNodes/ops/shared/anythingllm-sync
-alm-base (string)
AnythingLLM API base URL.
Default: http://127.0.0.1:3002/api/v1
-api-key (string)
AnythingLLM Bearer API key.
-once (bool)
Execute a single synchronization pass and exit immediately.
-interval (duration)
Continuous daemon run interval (e.g. 15m, 1h). Default 0 runs once.
-timeout (duration)
HTTP request timeout per operation.
Default: 30s
# One-shot scan and index
anythingllm-sync -projects /root/projects -api-key "$ANYTHINGLLM_API_KEY" -once
# Continuous daemon running every 30 minutes
anythingllm-sync -projects /root/projects -api-key "$ANYTHINGLLM_API_KEY" -interval 30mAdd to your MCP client configuration (e.g., Claude Desktop, Antigravity, or mcp-router):
{
"mcpServers": {
"anythingllm-control": {
"command": "/usr/local/bin/anythingllm-control",
"args": [],
"env": {
"ANYTHINGLLM_BASE_URL": "http://127.0.0.1:3002/api/v1",
"ANYTHINGLLM_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Run tests with data race detection and coverage:
# Run unit tests
make test
# Run tests with HTML/func coverage report
make coverage
# Run Go static analysis
make lintStrict Access Control: anythingllm-control contains destructive capabilities (delete_workspace). In production setups, it must only be exposed to trusted administrative agents or partitioned via mcp-router ACLs.
Prompt Injection Defense: Input strings for workspace names and slugs are sanitized to prevent path traversal and API injection.
Zero-Secret Leakage: API keys are injected via environment variables and never logged or serialized in error outputs.
MIT License — see LICENSE for full details.
Create a new workspace in AnythingLLM.
Args: name: Name for the new workspace.
Returns: JSON string with created workspace details or error message.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 only mentions that the tool returns a JSON string with details or an error message, but does not disclose permissions, side effects, or error scenarios beyond the generic message.
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 concise and front-loaded with the core action. The Args/Returns structure is clean, although the Args line is somewhat redundant with the schema.
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 one-parameter create tool with an output schema, the description covers the essential action and return type. However, it omits details like uniqueness constraints, name limitations, or preconditions that could affect usage in realistic workflows.
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 0%, so the description must compensate. The 'Args: name: Name for the new workspace' line adds only minimal meaning beyond the schema property's title 'Name' and does not specify constraints, uniqueness, or formatting requirements.
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 'Create a new workspace in AnythingLLM,' which clearly states the verb and resource. It distinguishes the tool from siblings like list_workspaces and delete_workspace, which perform different actions.
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?
No guidance is given on when to use this tool versus alternatives or when not to use it. The description simply states the action without any context, exclusions, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a workspace in AnythingLLM by slug.
Args: slug: The workspace slug to delete.
Returns: JSON string or status message confirming deletion or reporting error.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states that the workspace is deleted and a response is returned. It does not mention that deletion is permanent, irreversible, or has side effects, nor does it describe required permissions. For a destructive operation, this is a significant transparency 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 concise and clearly structured with Args and Returns sections. The Args section repeats schema information, but overall the text is front-loaded and free of unnecessary fluff. The slight redundancy prevents a perfect score.
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 tool is simple with only one parameter, and the description covers the basic action and return type. However, it lacks details on error handling, idempotency, or consequences of deleting a workspace. Given the presence of sibling tools and a basic schema, the description is minimally adequate but not rich.
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 0%, so the description must compensate. It merely restates the parameter name ('slug: The workspace slug to delete') without adding detail about what a slug is, how to obtain it, or accepted formats. This provides minimal added meaning over the schema field name.
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 ('Delete'), names the resource ('workspace'), and specifies the identifier ('by slug'). This clearly distinguishes it from sibling tools like list_workspaces and create_workspace.
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?
Usage is implied by the name and description: call this to delete a workspace. However, there is no explicit guidance on when to use this vs. alternatives, nor any prerequisites or conditions such as workspace existence. The description lacks exclusionary or contextual cues beyond the basic operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve system environment and configuration dump from AnythingLLM Admin API.
Returns: JSON string containing system environment variables and configuration.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only states 'Retrieve' and notes the return format, but does not explicitly declare read-only status, authentication requirements, or any potential risks (e.g., sensitive environment variables). The lack of such context makes it difficult to assess side effects or safety.
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 concise, consisting of just two short lines. It front-loads the primary purpose with a clear verb and resource, and the 'Returns' line efficiently communicates the output format without redundancy. Every sentence 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 tool with no parameters and an output schema already present, the description is largely complete. It states the purpose and return format. However, it lacks situational context such as noting that this is an admin-level read operation or that environment variables can be sensitive, which would be useful for an agent deciding to invoke it.
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 input schema is empty and the baseline for this dimension is 4. The description does not need to elaborate on parameters; it correctly focuses on the output, mentioning that it returns a JSON string of environment variables and configuration, which adds useful meaning beyond the 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 action ('Retrieve') and the specific resource ('system environment and configuration dump') from the AnythingLLM Admin API. This distinguishes it from sibling tools that operate on workspaces or vector counts, so it fully clarifies what the tool does.
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 provides no guidance on when to use this tool versus alternatives such as list_workspaces or get_vector_count. It does not mention context, prerequisites, or exclusions, leaving the agent without explicit usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get vector count for AnythingLLM (system-wide or workspace-specific).
Args: slug: Optional workspace slug. If provided, returns vector count for that workspace.
Returns: JSON string or status message with vector count stats.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears the burden. It discloses the operation is a read-only 'get' and indicates the return format (JSON string or status message). However, it doesn't detail potential failures, permission requirements, or performance implications, leaving some behavioral aspects undisclosed.
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 compact and structured with 'Args' and 'Returns' sections, avoiding unnecessary words while conveying the essential information. Every sentence 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?
Given the simplicity of a single optional parameter and the presence of an output schema, the description provides sufficient context to select and invoke the tool. It clearly states the system-wide vs workspace-specific distinction, and the sibling tools are unrelated so no additional differentiation is needed.
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 sole parameter 'slug' is fully explained in the description: optional workspace slug that when provided returns workspace-specific count. Since the schema only defines the parameter without descriptive text, this explanation adds crucial meaning and compensates for 0% schema coverage.
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 'Get vector count for AnythingLLM', a specific verb and resource, and clarifies scope as system-wide or workspace-specific. It is clearly distinct from sibling tools like list_workspaces or get_system_env, which serve different purposes.
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 explains that using the optional slug returns workspace-specific counts, implying that omitting it returns system-wide counts. This provides clear context for when to invoke with or without the parameter, though it doesn't explicitly mention exclusions or alternatives relative to sibling tools (which are unrelated).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all workspaces in AnythingLLM.
Returns: JSON string containing the list of workspaces or error status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It specifies the return format ('JSON string containing the list of workspaces or error status') but does not explicitly state that the operation is read-only, safe, or requires no special permissions. The verb 'list' implies non-destructive behavior, but this is not made explicit.
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 and followed by a return type description. There is no redundant information or verbose explanation.
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 no parameters and an output schema present, the description adequately covers the purpose and return format. It is sufficient for an agent to select and invoke the tool correctly, though it lacks explicit mention of any authorization or side-effect behavior.
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?
There are zero parameters, and the input schema is empty. The description adds no parameter details, but none are needed. The baseline of 4 applies because with no parameters, the description does not need to explain parameter 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 clearly states 'List all workspaces in AnythingLLM' with a specific verb and resource, distinguishing it from sibling tools like create_workspace and delete_workspace. There is no ambiguity about the tool's function.
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 listing workspaces but provides no explicit guidance on when to use it versus alternatives or any exclusions. It does not mention that creating or deleting workspaces should use sibling tools.
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.
v0.1.0create_workspacedelete_workspaceget_system_envget_vector_countlist_workspacesScored across 5 tools
Each tool targets a distinct resource and action: workspace CRUD operations (list/create/delete) are clearly separated from system environment and vector count queries. No two tools have overlapping purposes, making selection unambiguous.
All tool names follow a consistent verb_noun pattern in snake_case: list_workspaces, create_workspace, delete_workspace, get_system_env, get_vector_count. The verbs and nouns are predictable and match the action/resource pair.
Five tools is well-scoped for a control plane server, covering core workspace management and system information without unnecessary bloat. The count feels appropriate for the apparent domain.
The workspace lifecycle is incomplete: create, list, and delete exist, but update is missing. System environment is read-only, and there is no way to modify configuration. These gaps are notable for a control plane server, though the core viewing/deleting operations are present.
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