MCP Server

Hindsight includes a built-in Model Context Protocol (MCP) server that allows AI assistants to store and retrieve memories directly.

Access

The MCP server is enabled by default and mounted at /mcp on the API server. Each memory bank has its own MCP endpoint:

http://localhost:8888/mcp/{bank_id}/

For example, to connect to the memory bank alice:

http://localhost:8888/mcp/alice/

To disable the MCP server, set the environment variable:

export HINDSIGHT_API_MCP_ENABLED=false

Authentication

By default, the MCP endpoint is open (no authentication required).

To enable authentication, configure the API key tenant extension:

export HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
export HINDSIGHT_API_TENANT_API_KEY=your-secret-key

When authentication is enabled, include your API key in the Authorization header:

Claude Code

claude mcp add --transport http hindsight http://localhost:8888/mcp \
  --header "Authorization: Bearer your-secret-key" \
  --header "X-Bank-Id: my-bank"

Claude Desktop

Add to ~/.claude_desktop_config.json:

{
  "mcpServers": {
    "hindsight": {
      "url": "http://localhost:8888/mcp",
      "headers": {
        "Authorization": "Bearer your-secret-key",
        "X-Bank-Id": "my-bank"
      }
    }
  }
}

Direct HTTP Request

curl -X POST http://localhost:8888/mcp \
  -H "Authorization: Bearer your-secret-key" \
  -H "X-Bank-Id: my-bank" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc": "2.0", "method": "tools/list", "id": 1}'

If the key is missing or invalid, requests will receive a 401 Unauthorized response.

Bank Selection

The memory bank is resolved in this priority order:

1. URL path (highest priority): http://localhost:8888/mcp/my-bank/
2. X-Bank-Id header: --header "X-Bank-Id: my-bank"
3. Default: Uses HINDSIGHT_MCP_BANK_ID env var (default: "default")

Per-Bank Endpoints

Unlike traditional MCP servers where tools require explicit identifiers, Hindsight uses per-bank endpoints. The bank_id is part of the URL path, so tools don't need to specify which bank to use—it's implicit from the connection.

This design:

• Simplifies tool usage — no need to pass bank_id with every call
• Enforces isolation — each MCP connection is scoped to a single bank
• Enables multi-tenant setups — connect different users to different endpoints

Two Modes

The MCP server operates in two modes depending on the URL:

Mode	URL	Tools	bank_id
Single-bank	/mcp/{bank_id}/	27 tools (memory, mental models, directives, documents, operations, tags, bank management)	Implicit from URL
Multi-bank	/mcp/	All 30 tools including list_banks, create_bank, get_bank_stats	Explicit bank_id parameter on each tool

Single-bank mode (recommended) scopes all operations to the bank in the URL. Tools don't expose a bank_id parameter.

Multi-bank mode exposes all tools with an optional bank_id parameter, plus bank management tools (list_banks, create_bank, get_bank_stats).

Tool Metadata and Instructions

Hindsight can append deployment-specific guidance to the retain and recall MCP tool descriptions. Set HINDSIGHT_API_MCP_INSTRUCTIONS on the API server when clients should see local rules, such as which tags to use or which memories should be retained.

export HINDSIGHT_API_MCP_INSTRUCTIONS="Use project:<name> tags for project-specific memories."

MCP clients that read tool annotations also receive safety hints from the built-in tools:

• Read-only operations such as recall, reflect, list_*, and get_* are marked with readOnlyHint: true.
• Delete, clear, and invalidate operations are marked with destructiveHint: true.
• openWorldHint is false for the built-in tools because Hindsight operates on its configured memory store rather than the open internet.
• Write operations such as retain, create_*, update_*, refresh_mental_model, and cancel_operation are not marked destructive.

---

Available Tools

retain

Store information to long-term memory.

Parameter	Type	Required	Description
content	string	Yes	The fact or memory to store
context	string	No	Category for the memory (default: general)
timestamp	string	No	ISO 8601 timestamp for when the event occurred
tags	list[string]	No	Tags for organizing and filtering this memory
metadata	object	No	Key-value metadata to attach (e.g., {"source": "slack"})
document_id	string	No	Associate this memory with an existing document

Example:

{
  "name": "retain",
  "arguments": {
    "content": "User prefers Python over JavaScript for backend development",
    "context": "programming_preferences",
    "tags": ["user:alice", "preferences"]
  }
}

When to use:

• User shares personal facts, preferences, or interests
• Important events or milestones are mentioned
• Decisions, opinions, or goals are stated
• Work context or project details are discussed

---

sync_retain

Store information to long-term memory and wait for completion. Unlike retain (which is asynchronous), sync_retain blocks until the memory is fully stored and immediately available for recall — useful for read-after-write flows where you query right after storing.

Parameter	Type	Required	Description
content	string	Yes	The fact or memory to store
context	string	No	Category for the memory (default: general)
timestamp	string	No	ISO 8601 timestamp for when the event occurred
tags	list[string]	No	Tags for organizing and filtering this memory
metadata	object	No	Key-value metadata to attach (e.g., {"source": "slack"})
document_id	string	No	Associate this memory with an existing document

Example:

{
  "name": "sync_retain",
  "arguments": {
    "content": "User prefers Python over JavaScript for backend development",
    "context": "programming_preferences",
    "tags": ["user:alice", "preferences"]
  }
}

When to use:

• You need the memory queryable immediately after storing (read-after-write)
• A workflow step depends on the stored memory being available before continuing
• Otherwise prefer retain (asynchronous) to avoid blocking on storage

---

recall

Search memories to provide personalized responses.

Parameter	Type	Required	Description
query	string	Yes	Natural language search query
max_tokens	integer	No	Token budget for the facts' text (default: 4096). Per-result metadata (id, tags, scores, dates) is not counted, so the response is several times larger
budget	string	No	Search thoroughness: low, mid, or high (default: high)
types	list[string]	No	Filter by fact type: world, experience, observation. Defaults to all
tags	list[string]	No	Filter memories by tags. Omit for no filter
tags_match	string	No	any (default), all, any_strict, all_strict, or exact. With exact, pass tags: [] to select the untagged/global scope
tag_groups	list[object]	No	Compound boolean tag filter. Mutually exclusive with tags; each leaf has its own match value
query_timestamp	string	No	ISO 8601 timestamp — recall as if asking at this point in time; anchors relative temporal expressions and recency scoring
min_scores	object	No	Optional per-stage score floors, e.g. {"reranker": 0.5}. Keys: semantic/keyword (retrieval-level cutoffs), reranker/final (post-ranking). All inclusive and AND-ed; omit for no filtering. Reranker scores aren't calibrated across queries — calibrate before use
temporal_window	object	No	An explicit {"start": ISO, "end": ISO} period to search over, used instead of reading dates out of the query text. Ranks memories dated inside the window higher; it does not drop the ones outside it, so it can't restrict results to a period

Example:

{
  "name": "recall",
  "arguments": {
    "query": "What are the user's programming language preferences?",
    "tags": ["preferences"],
    "budget": "high"
  }
}

When to use:

• Start of conversation to recall relevant context
• Before making recommendations
• When user asks about something they may have mentioned before
• To provide continuity across conversations

---

reflect

Generate thoughtful analysis by synthesizing stored memories with the bank's personality.

Parameter	Type	Required	Description
query	string	Yes	The question or topic to reflect on
context	string	No	Optional context about why this reflection is needed
budget	string	No	Search budget: low, mid, or high (default: low)
max_tokens	integer	No	Maximum tokens in the response (default: 4096)
response_schema	object	No	JSON Schema for structured output. When provided, the response includes a structured_output field
tags	list[string]	No	Scope memories, observations, mental models, and tagged directives. Omitted tags leave memory retrieval unfiltered but load only untagged directives
tags_match	string	No	any (default), all, any_strict, all_strict, or exact. Untagged directives remain global in every mode
include_trace	boolean	No	Include tool_trace and llm_trace debugging output. Defaults to false to keep responses small

The MCP tool forwards tags_match only when tags is present. Pass tags: [] with tags_match: "exact" to select the empty/global scope for raw facts, observations, and mental models; directive loading also selects only untagged directives.

Example:

{
  "name": "reflect",
  "arguments": {
    "query": "Based on my past decisions, what architectural style do I prefer?",
    "budget": "mid",
    "tags": ["architecture"]
  }
}

When to use:

• When reasoned analysis is needed, not just fact retrieval
• Questions like "What should I do?" rather than "What did I say?"
• Synthesizing patterns across multiple memories

---

create_mental_model

Create a mental model — a living document that stays current with your memories. Mental models are pre-computed reflections that get automatically refreshed as new memories are stored.

Parameter	Type	Required	Description
name	string	Yes	Human-readable name for the mental model
source_query	string	Yes	The query used to generate and refresh the model
mental_model_id	string	No	Custom ID (alphanumeric lowercase with hyphens). Auto-generated if not provided
tags	list[string]	No	Tags for organizing and filtering models
tags_match	string	No	How the model's tags are matched against memories on refresh: any, all, any_strict, all_strict, or exact. See the note below on the default
trigger	object	No	Refresh policy — see Trigger settings
max_tokens	integer	No	Maximum tokens for model content (default: 2048)
trigger_refresh_after_consolidation	boolean	No	Legacy shorthand for trigger.refresh_after_consolidation

trigger is the preferred form for refresh settings; tags_match and trigger_refresh_after_consolidation remain as shorthands for existing integrations. Setting the same field both ways is an error rather than one silently winning.

Tagged models default to all_strict

When a mental model has tags but no explicit tags_match, its refresh matches memories with all_strict — a memory must carry every one of the model's tags to be included. If your memories use narrow, single-topic tags (e.g. ["project:status"]) while the model is tagged broadly (e.g. ["projects", "mental-model"]), the refresh filters out everything and the content comes back empty.

Pass tags_match: "any" (the same default that recall and reflect use) to match memories that carry any of the model's tags:

{
  "name": "create_mental_model",
  "arguments": {
    "name": "Current projects",
    "source_query": "Which projects is the user currently working on?",
    "tags": ["projects", "mental-model"],
    "tags_match": "any"
  }
}

Example:

{
  "name": "create_mental_model",
  "arguments": {
    "name": "Team Directory",
    "source_query": "Who works here and what do they do?",
    "tags": ["team", "people"],
    "tags_match": "any"
  }
}

Content generation runs asynchronously. The response includes an operation_id to track progress.

---

list_mental_models

List all mental models in a bank, optionally filtered by tags.

Returns metadata only — id, name, tags, is_stale and timestamps. To read a model's synthesized content, call get_mental_model with an id from this list.

Parameter	Type	Required	Description
tags	list[string]	No	Filter models by tags

---

get_mental_model

Retrieve a specific mental model by ID, including its full content.

Parameter	Type	Required	Description
mental_model_id	string	Yes	The ID of the mental model to retrieve

---

update_mental_model

Update a mental model's metadata or settings.

Parameter	Type	Required	Description
mental_model_id	string	Yes	The ID of the mental model to update
name	string	No	New name
source_query	string	No	New source query
tags	list[string]	No	New tags
max_tokens	integer	No	New max tokens
trigger	object	No	Refresh-policy fields to change — see Trigger settings. This is a patch: omitted fields keep their current values
tags_match	string	No	Legacy shorthand for trigger.tags_match
trigger_refresh_after_consolidation	boolean	No	Auto-refresh after consolidation. Only set when you want to change this setting

---

Trigger settings

trigger carries a mental model's (or knowledge page's) refresh policy: when it rebuilds itself and what it rebuilds from. It accepts every field the HTTP API accepts, so anything you can configure through PATCH /mental_models/{id} you can also configure from an agent over MCP.

Field	Type	Description
mode	string	full regenerates the content from scratch on each refresh; delta makes surgical edits, preserving unchanged sections byte-for-byte. Delta falls back to full when there is no existing content or the source query changed
refresh_after_consolidation	boolean	Rebuild after each memory consolidation
refresh_cron	string	UTC five-field cron, e.g. 0 3 * * * for daily at 03:00 UTC. Only runs when the model is stale, so an unchanged scope costs no LLM call
min_refresh_interval_seconds	integer	Floor between two automatic refreshes. Triggers arriving sooner fold into one queued refresh, so a burst of retains costs one rebuild. Explicit refreshes ignore it
fact_types	list[string]	Which of world, experience, observation the refresh retrieves. Omit for all three
exclude_mental_models	boolean	Exclude all mental models from the refresh, so a model never reflects on its siblings
exclude_mental_model_ids	list[string]	Exclude specific mental models by ID
tags_match	string	How the model's tags select memories: any, all, any_strict, all_strict, exact
tag_groups	list[object]	Compound boolean tag expressions used instead of the model's flat tags
include_chunks	boolean	Whether the refresh's internal recall returns raw chunk text
recall_max_tokens	integer	Token budget for facts from the refresh's internal recall
recall_chunks_max_tokens	integer	Token budget for raw chunks from the refresh's internal recall
budget	string	How many agent steps a refresh may spend, as a multiple of the server's reflect iteration limit: low halves it, mid keeps it, high doubles it. Omit for mid
response_schema	object	JSON Schema for structured output, stored alongside the markdown under reflect_response.structured_output
keep_trace	boolean	Record how each refresh reached its result under reflect_response.trace. The only way to diagnose a cron- or consolidation-driven refresh after the fact

refresh_after_consolidation and refresh_cron are mutually exclusive — a model refreshes either after consolidation or on a schedule, never both. Setting one clears the other.

On create, omitted fields take the engine defaults (mode: full, no schedule, all fact types); for a knowledge page they take the page defaults (mode: delta, observation-only, auto-refresh, siblings excluded).

On update, trigger is a patch: only the fields you send change, so putting a model on a cron schedule keeps its fact_types and mode. Send an explicit null to clear a nullable setting. Read the current policy back with get_mental_model (detail content or full) if you want to inspect before changing.

{
  "name": "update_mental_model",
  "arguments": {
    "mental_model_id": "team-conventions",
    "trigger": { "refresh_cron": "0 3 * * *", "fact_types": ["observation"] }
  }
}

---

delete_mental_model

Permanently delete a mental model.

Parameter	Type	Required	Description
mental_model_id	string	Yes	The ID of the mental model to delete

---

refresh_mental_model

Re-generate a mental model's content from the latest memories. Runs asynchronously.

Parameter	Type	Required	Description
mental_model_id	string	Yes	The ID of the mental model to refresh

---

clear_mental_model

Clear a mental model's content while keeping its definition. After clearing, call refresh_mental_model to rebuild it from the latest memories.

Parameter	Type	Required	Description
mental_model_id	string	Yes	The ID of the mental model to clear

---

list_banks (multi-bank mode only)

List available memory banks, most recently written first. The response carries the total number of matching banks alongside the page, so large deployments can be walked with offset.

Parameter	Type	Required	Description
query	string	No	Case-insensitive substring matched against bank ID and name
limit	integer	No	Maximum number of banks to return (default: 100)
offset	integer	No	Number of banks to skip (default: 0)

---

create_bank (multi-bank mode only)

Create a new memory bank or retrieve an existing one.

Parameter	Type	Required	Description
bank_id	string	Yes	The ID for the new bank
name	string	No	Human-friendly name for the bank
mission	string	No	Mission describing who the agent is and what they're trying to accomplish

---

list_directives

List directives in a bank. This management tool does not use reflect's directive-isolation behavior: omitting tags lists every directive, including tagged directives.

Parameter	Type	Required	Description
tags	list[string]	No	Filter using any matching. When present, returns untagged/global directives plus directives sharing at least one tag. When omitted or empty, returns all directives
active_only	boolean	No	Only return active directives (default: true)

---

create_directive

Create a new directive in a bank.

Parameter	Type	Required	Description
name	string	Yes	Human-readable name for the directive
content	string	Yes	The directive content/instruction
priority	integer	No	Priority level (higher = more important)
is_active	boolean	No	Whether the directive is active (default: true)
tags	list[string]	No	Execution scope for the directive. Empty/omitted (default) means global; non-empty means reflect must use a matching tag scope

---

delete_directive

Delete a directive by ID.

Parameter	Type	Required	Description
directive_id	string	Yes	The ID of the directive to delete

---

list_memories

Browse stored memories with optional filtering and pagination.

Parameter	Type	Required	Description
type	string	No	Filter by fact type: world, experience, or observation
q	string	No	Search query to filter memories
limit	integer	No	Maximum number of results (default: 100)
offset	integer	No	Number of results to skip for pagination (default: 0)

---

get_memory

Retrieve a specific memory by ID.

Parameter	Type	Required	Description
memory_id	string	Yes	The ID of the memory to retrieve

---

list_documents

List documents that have been ingested into the memory bank.

Parameter	Type	Required	Description
q	string	No	Search query to filter documents
limit	integer	No	Maximum number of results (default: 100)
offset	integer	No	Number of results to skip for pagination (default: 0)

---

get_document

Retrieve a specific document by ID, including its metadata.

Parameter	Type	Required	Description
document_id	string	Yes	The ID of the document to retrieve

---

delete_document

Delete a document and all memories linked to it.

Parameter	Type	Required	Description
document_id	string	Yes	The ID of the document to delete

---

list_operations

List async operations (retain processing, mental model refresh, etc.) with optional status and type filtering.

Parameter	Type	Required	Description
status	string	No	Filter by status: pending, running, completed, failed, cancelled
type	string	No	Filter by type: retain, consolidation, refresh_mental_model, file_convert_retain, webhook_delivery
limit	integer	No	Maximum number of results, 1–100 (default: 20)
offset	integer	No	Number of results to skip for pagination (default: 0)
exclude_parents	boolean	No	Exclude parent batch operations (default: false)

---

get_operation

Get the status and details of an async operation.

Parameter	Type	Required	Description
operation_id	string	Yes	The ID of the operation to check

---

cancel_operation

Cancel a pending or running async operation.

Parameter	Type	Required	Description
operation_id	string	Yes	The ID of the operation to cancel

---

list_tags

List all unique tags used in a bank, optionally filtered by pattern.

Parameter	Type	Required	Description
q	string	No	Glob pattern to filter tags (e.g., project:*)
limit	integer	No	Maximum number of results (default: 100)
offset	integer	No	Number of results to skip for pagination (default: 0)

---

get_bank

Get information about a memory bank, including its name, mission, and disposition.

---

get_bank_stats (multi-bank mode only)

Get statistics for a memory bank (node/link counts).

---

update_bank

Update a memory bank's configuration. Updates the bank's name and/or any bank-level configuration fields — only provided fields are updated; omitted fields remain unchanged.

Parameter	Type	Required	Description
name	string	No	Human-friendly display name for the bank
mission	string	No	Deprecated — alias for config_updates.reflect_mission
config_updates	object	No	Dictionary of configuration fields to update. Supports all bank-configurable fields (see below). Non-configurable or credential fields are rejected

The config_updates object accepts any bank-configurable field by its Python field name, including:

• reflect_mission — mission/context for Reflect operations
• retain_mission — steers what gets extracted during retain()
• retain_extraction_mode — concise (default), verbose, custom, verbatim, or chunks
• retain_custom_instructions — custom extraction prompt (active when mode is custom)
• retain_chunk_size — target maximum characters for each content chunk
• retain_structured_chunk_size — maximum characters for a single JSONL line or conversation turn to keep whole
• retain_chunk_batch_size — number of chunks to process in parallel
• enable_observations — toggle observation consolidation after retain()
• observations_mission — controls observation synthesis rules
• disposition_skepticism — critical evaluation level (1–5)
• disposition_literalism — literal vs. abstract interpretation (1–5)
• disposition_empathy — emotional context consideration (1–5)
• entity_labels — controlled vocabulary for entity classification
• entities_allow_free_form — allow labels outside entity_labels
• recall_include_chunks — include raw chunks in recall results
• recall_max_tokens — max tokens for recall results
• mcp_enabled_tools — tool allowlist for this bank

---

delete_bank

Permanently delete a memory bank and all its data (memories, documents, entities, mental models).

---

clear_memories

Clear all memories from a bank without deleting the bank itself. Optionally filter by fact type to only clear specific kinds of memories.

Parameter	Type	Required	Description
type	string	No	Fact type to clear: world, experience, or observation. If not specified, clears all

---

get_knowledge_base_tree

Browse the knowledge base as a nested tree of folders and pages. Each page reports is_stale: false means it is provably up to date, true means the bank has been written to since the page last refreshed.

---

search_knowledge_base

Find knowledge pages by relevance (hybrid BM25 + vector search over page names and content). Returns ranked pages with a snippet each.

Parameter	Type	Required	Description
query	string	Yes	What to search for
limit	integer	No	Maximum pages to return, 1–50 (default: 10)

---

get_knowledge_page

Read a knowledge page as a markdown document (YAML frontmatter + synthesized body).

Parameter	Type	Required	Description
page_id	string	Yes	The ID of the page to read (a kp-... node id)

---

create_knowledge_folder

Create a folder in the knowledge base. Folders group pages and hold no content of their own.

Parameter	Type	Required	Description
name	string	Yes	Folder name
parent_id	string	No	Parent folder id (a kf-... node id). Omit to create at the top level

---

create_knowledge_page

Create a page — a living document whose content is synthesized from the bank's memories by running source_query. Content is generated asynchronously; use the returned operation_id to track completion.

Parameter	Type	Required	Description
name	string	Yes	Page name (unique within its folder)
source_query	string	Yes	The question this page answers and rebuilds itself from
parent_id	string	No	Parent folder id (a kf-... node id). Omit to create at the top level
tags	array	No	Tags scoping which memories the page is built from
max_tokens	integer	No	Maximum tokens for the generated content (default: 4096)
trigger	object	No	Refresh policy — see Trigger settings. Omitted fields keep the knowledge-page defaults: delta rebuilds from consolidated observations after each consolidation, ignoring sibling pages
refresh_after_consolidation	boolean	No	Legacy shorthand for trigger.refresh_after_consolidation

Set trigger.refresh_cron to move a page off consolidation-driven rebuilds and onto a fixed UTC schedule — the two are mutually exclusive, so the cron clears the auto-refresh while leaving the page's mode and fact_types alone.

---

update_knowledge_node

Rename or move a folder/page, and/or update a page's options. Only the arguments you pass are changed. Changing source_query schedules an async refresh so the page rebuilds against the new question.

Parameter	Type	Required	Description
node_id	string	Yes	The folder (kf-...) or page (kp-...) to update
name	string	No	New name for the node
parent_id	string	No	Folder id to move the node into, or "root" to move it to the top level
source_query	string	No	Pages only — the new question the page answers
tags	array	No	Pages only — replacement tag list (pass [] to clear)
max_tokens	integer	No	Pages only — new maximum tokens for the generated content
trigger	object	No	Pages only — refresh-policy fields to change; see Trigger settings. This is a patch: omitted fields keep their current values
refresh_after_consolidation	boolean	No	Pages only — legacy shorthand for trigger.refresh_after_consolidation

---

delete_knowledge_node

Delete a folder or page and its whole subtree. Each deleted page takes its backing mental model with it.

Parameter	Type	Required	Description
node_id	string	Yes	The folder (kf-...) or page (kp-...) to delete

note

Exporting the knowledge base is deliberately not an MCP tool — it returns the whole bank as a single markdown bundle. Use the HTTP endpoint GET /v1/default/banks/{bank_id}/knowledge-base/export instead.

---

Integration with AI Assistants

The MCP server can be used with any MCP-compatible AI assistant. See the Authentication section above for Claude Code and Claude Desktop configuration examples.

Each user can have their own configuration pointing to their personal memory bank using either:

• A bank-specific URL path like /mcp/alice/ (recommended)
• The X-Bank-Id header