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<h1 id="cortexgraph-mcp-tools-reference">CortexGraph MCP Tools Reference<a class="headerlink" href="#cortexgraph-mcp-tools-reference" title="Permanent link">¶</a></h1>
<p><strong>Comprehensive documentation for all CortexGraph MCP server tools.</strong></p>
<p>This document contains the full technical reference for all 18 MCP tools provided by CortexGraph. For minimal tool schemas in the MCP server itself, see the source files in <code>src/cortexgraph/tools/</code>.</p>
<hr />
<h2 id="table-of-contents">Table of Contents<a class="headerlink" href="#table-of-contents" title="Permanent link">¶</a></h2>
<ol>
<li><a href="#memory-management">Memory Management</a></li>
<li>save_memory</li>
<li>search_memory</li>
<li>search_unified</li>
<li>touch_memory</li>
<li>open_memories</li>
<li>
<p>read_graph</p>
</li>
<li>
<p><a href="#analysis-tools">Analysis Tools</a></p>
</li>
<li>analyze_message</li>
<li>analyze_for_recall</li>
<li>
<p>auto_recall_process_message</p>
</li>
<li>
<p><a href="#graph-operations">Graph Operations</a></p>
</li>
<li>cluster_memories</li>
<li>consolidate_memories</li>
<li>
<p>create_relation</p>
</li>
<li>
<p><a href="#maintenance">Maintenance</a></p>
</li>
<li>gc</li>
<li>promote_memory</li>
<li>
<p>backfill_embeddings</p>
</li>
<li>
<p><a href="#performance">Performance</a></p>
</li>
<li>get_performance_metrics</li>
<li>reset_performance_metrics</li>
</ol>
<hr />
<h2 id="memory-management">Memory Management<a class="headerlink" href="#memory-management" title="Permanent link">¶</a></h2>
<h3 id="save_memory">save_memory<a class="headerlink" href="#save_memory" title="Permanent link">¶</a></h3>
<p>Save a new memory to short-term storage with automatic preprocessing.</p>
<p>The memory will have temporal decay applied and will be forgotten if not used regularly. Frequently accessed memories may be promoted to long-term storage automatically.</p>
<p><strong>Auto-enrichment (v0.6.0)</strong>: If entities or strength are not provided, they will be automatically extracted/calculated from the content using natural language preprocessing. This makes save_memory "just work" for conversational use.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>content</code> (str): The content to remember (max 50,000 chars).</li>
<li><code>tags</code> (list[str] | None): Tags for categorization (max 50 tags, each max 100 chars).</li>
<li><code>entities</code> (list[str] | None): Named entities in this memory (max 100 entities). If None, automatically extracted from content.</li>
<li><code>source</code> (str | None): Source of the memory (max 500 chars).</li>
<li><code>context</code> (str | None): Context when memory was created (max 1,000 chars).</li>
<li><code>meta</code> (dict[str, Any] | None): Additional custom metadata.</li>
<li><code>strength</code> (float | None): Base strength multiplier (1.0-2.0). If None, automatically calculated based on content importance.</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Dictionary with:
- <code>success</code> (bool): Whether operation succeeded
- <code>memory_id</code> (str): UUID of saved memory
- <code>message</code> (str): Human-readable confirmation
- <code>has_embedding</code> (bool): Whether embedding was generated
- <code>enrichment_applied</code> (bool): Whether auto-enrichment was used
- <code>auto_entities</code> (int): Number of entities auto-extracted
- <code>calculated_strength</code> (float): Final strength value</p>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If any input fails validation.</li>
</ul>
<hr />
<h3 id="search_memory">search_memory<a class="headerlink" href="#search_memory" title="Permanent link">¶</a></h3>
<p>Search for memories with optional filters and scoring.</p>
<p>This tool implements natural spaced repetition by blending memories due for review into results when they're relevant. This creates the "Maslow effect" - natural reinforcement through conversation.</p>
<p><strong>Content Preview (v0.7.0):</strong> By default, returns first 300 characters of each memory to reduce context usage. Pass <code>preview_length=0</code> for full content, or set a custom length (1-5000 characters).</p>
<p><strong>Pagination:</strong> Results are paginated to help you find specific memories across large result sets. Use <code>page</code> and <code>page_size</code> to navigate through results. If a search term isn't found on the first page, increment <code>page</code> to see more results.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>query</code> (str | None): Text query to search for (max 50,000 chars).</li>
<li><code>tags</code> (list[str] | None): Filter by tags (max 50 tags).</li>
<li><code>top_k</code> (int): Maximum number of results before pagination (1-100).</li>
<li><code>window_days</code> (int | None): Only search memories from last N days (1-3650).</li>
<li><code>min_score</code> (float | None): Minimum decay score threshold (0.0-1.0).</li>
<li><code>use_embeddings</code> (bool): Use semantic search with embeddings.</li>
<li><code>include_review_candidates</code> (bool): Blend in memories due for review (default True).</li>
<li><code>page</code> (int | None): Page number to retrieve (1-indexed, default: 1).</li>
<li><code>page_size</code> (int | None): Number of memories per page (default: 10, max: 100).</li>
<li><code>preview_length</code> (int | None): Content preview length in chars (default: 300, 0 = full content).</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Dictionary with paginated results including:
- <code>results</code> (list): List of matching memories with scores for current page
- <code>pagination</code> (dict): Metadata (page, page_size, total_count, total_pages, has_more)</p>
<p>Some results may be review candidates that benefit from reinforcement.</p>
<p><strong>Examples:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="c1"># Get first page with previews (default 300 chars)</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">search_memory</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="s2">"authentication"</span><span class="p">,</span> <span class="n">page</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="c1"># Get full content</span>
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="n">search_memory</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="s2">"authentication"</span><span class="p">,</span> <span class="n">preview_length</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a>
<a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="c1"># Custom preview length</span>
<a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="n">search_memory</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="s2">"authentication"</span><span class="p">,</span> <span class="n">preview_length</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
</code></pre></div>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If any input fails validation.</li>
</ul>
<hr />
<h3 id="search_unified">search_unified<a class="headerlink" href="#search_unified" title="Permanent link">¶</a></h3>
<p>Search across both STM and LTM with unified ranking.</p>
<p><strong>Content Preview (v0.7.0):</strong> By default, returns first 300 characters of each memory to reduce context usage. Pass <code>preview_length=0</code> for full content, or set a custom length (1-5000 characters).</p>
<p><strong>Pagination:</strong> Results are paginated to help you find specific memories across large result sets from both short-term and long-term memory. Use <code>page</code> and <code>page_size</code> to navigate through results. If a search term isn't found on the first page, increment <code>page</code> to see more results.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>query</code> (str | None): Text query to search for (max 50,000 chars).</li>
<li><code>tags</code> (list[str] | None): Filter by tags (max 50 tags).</li>
<li><code>limit</code> (int): Maximum total results before pagination (1-100).</li>
<li><code>stm_weight</code> (float): Weight multiplier for STM results (0.0-2.0).</li>
<li><code>ltm_weight</code> (float): Weight multiplier for LTM results (0.0-2.0).</li>
<li><code>window_days</code> (int | None): Only include STM memories from last N days (1-3650).</li>
<li><code>min_score</code> (float | None): Minimum score threshold for STM memories (0.0-1.0).</li>
<li><code>page</code> (int | None): Page number to retrieve (1-indexed, default: 1).</li>
<li><code>page_size</code> (int | None): Number of memories per page (default: 10, max: 100).</li>
<li><code>preview_length</code> (int | None): Content preview length in chars (default: 300, 0 = full content).</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Dictionary with paginated results including:
- <code>results</code> (list): List of matching memories from STM and LTM for current page
- <code>pagination</code> (dict): Metadata (page, page_size, total_count, total_pages, has_more)</p>
<p><strong>Examples:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="c1"># Get first page with previews (default 300 chars)</span>
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="n">search_unified</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="s2">"architecture"</span><span class="p">,</span> <span class="n">page</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="c1"># Get full content</span>
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a><span class="n">search_unified</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="s2">"architecture"</span><span class="p">,</span> <span class="n">preview_length</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</code></pre></div>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If any input fails validation.</li>
</ul>
<hr />
<h3 id="touch_memory">touch_memory<a class="headerlink" href="#touch_memory" title="Permanent link">¶</a></h3>
<p>Reinforce a memory by updating its last accessed time and use count.</p>
<p>This resets the temporal decay and increases the memory's resistance to being forgotten. Optionally can boost the memory's base strength.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>memory_id</code> (str): ID of the memory to reinforce (valid UUID).</li>
<li><code>boost_strength</code> (bool): Whether to boost the base strength.</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Updated memory statistics including old and new scores.</p>
<p>Dictionary with:
- <code>success</code> (bool): Whether operation succeeded
- <code>memory_id</code> (str): UUID of reinforced memory
- <code>old_score</code> (float): Score before reinforcement
- <code>new_score</code> (float): Score after reinforcement
- <code>use_count</code> (int): Updated use count
- <code>strength</code> (float): Updated strength value
- <code>message</code> (str): Human-readable summary</p>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If memory_id is invalid.</li>
</ul>
<hr />
<h3 id="open_memories">open_memories<a class="headerlink" href="#open_memories" title="Permanent link">¶</a></h3>
<p>Retrieve specific memories by their IDs.</p>
<p>Similar to the reference MCP memory server's open_nodes functionality. Returns detailed information about the requested memories including their relations to other memories.</p>
<p><strong>Pagination:</strong> When retrieving many memories by ID, results are paginated. Use <code>page</code> and <code>page_size</code> to navigate through the list of requested memories.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>memory_ids</code> (str | list[str]): Single memory ID or list of memory IDs to retrieve (max 100 IDs).</li>
<li><code>include_relations</code> (bool): Include relations from/to these memories.</li>
<li><code>include_scores</code> (bool): Include decay scores and age.</li>
<li><code>page</code> (int | None): Page number to retrieve (1-indexed, default: 1).</li>
<li><code>page_size</code> (int | None): Number of memories per page (default: 10, max: 100).</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Dictionary with paginated results including:
- <code>memories</code> (list): Detailed memory information for current page
- <code>not_found</code> (list): List of IDs that weren't found
- <code>pagination</code> (dict): Metadata (page, page_size, total_count, total_pages, has_more)</p>
<p><strong>Examples:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="c1"># Get first page of memories</span>
<a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="n">open_memories</span><span class="p">([</span><span class="s2">"id1"</span><span class="p">,</span> <span class="s2">"id2"</span><span class="p">,</span> <span class="s2">"id3"</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">page</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>
<a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="c1"># Get next page</span>
<a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a><span class="n">open_memories</span><span class="p">([</span><span class="s2">"id1"</span><span class="p">,</span> <span class="s2">"id2"</span><span class="p">,</span> <span class="s2">"id3"</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">page</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</code></pre></div>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If any memory ID is invalid or list exceeds maximum length.</li>
</ul>
<hr />
<h3 id="read_graph">read_graph<a class="headerlink" href="#read_graph" title="Permanent link">¶</a></h3>
<p>Read the entire knowledge graph of memories and relations.</p>
<p>Returns the complete graph structure including all memories (with decay scores), all relations between memories, and statistics about the graph.</p>
<p><strong>Pagination:</strong> Results are paginated to help you navigate large knowledge graphs. Use <code>page</code> and <code>page_size</code> to retrieve specific portions of the graph. If searching for specific memories or patterns, increment <code>page</code> to see more results.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>status</code> (str): Filter memories by status - "active", "promoted", "archived", or "all".</li>
<li><code>include_scores</code> (bool): Include decay scores and age in results.</li>
<li><code>limit</code> (int | None): Maximum number of memories to return (1-10,000).</li>
<li><code>page</code> (int | None): Page number to retrieve (1-indexed, default: 1).</li>
<li><code>page_size</code> (int | None): Number of memories per page (default: 10, max: 100).</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Dictionary with paginated graph including:
- <code>memories</code> (list): List of memories for current page
- <code>relations</code> (list): All relations (not paginated, for graph structure)
- <code>stats</code> (dict): Graph statistics
- <code>pagination</code> (dict): Metadata (page, page_size, total_count, total_pages, has_more)</p>
<p><strong>Examples:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="c1"># Get first page of active memories</span>
<a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a><span class="n">read_graph</span><span class="p">(</span><span class="n">status</span><span class="o">=</span><span class="s2">"active"</span><span class="p">,</span> <span class="n">page</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a>
<a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="c1"># Get next page</span>
<a id="__codelineno-3-5" name="__codelineno-3-5" href="#__codelineno-3-5"></a><span class="n">read_graph</span><span class="p">(</span><span class="n">status</span><span class="o">=</span><span class="s2">"active"</span><span class="p">,</span> <span class="n">page</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a>
<a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a><span class="c1"># Larger page for overview</span>
<a id="__codelineno-3-8" name="__codelineno-3-8" href="#__codelineno-3-8"></a><span class="n">read_graph</span><span class="p">(</span><span class="n">status</span><span class="o">=</span><span class="s2">"active"</span><span class="p">,</span> <span class="n">page</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
</code></pre></div>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If status is invalid or limit is out of range.</li>
</ul>
<hr />
<h2 id="analysis-tools">Analysis Tools<a class="headerlink" href="#analysis-tools" title="Permanent link">¶</a></h2>
<h3 id="analyze_message">analyze_message<a class="headerlink" href="#analyze_message" title="Permanent link">¶</a></h3>
<p>Analyze a message to determine if it contains memory-worthy content.</p>
<p>Returns activation signals and suggested parameters for save_memory. This tool helps the LLM decide whether to save information without explicit "remember this" commands.</p>
<p><strong>Decision Support (v0.6.0)</strong>: Provides confidence scores and reasoning to help Claude determine if save_memory should be called. High confidence (>0.7) suggests automatic save; medium confidence (0.4-0.7) suggests asking user first.</p>
<p><strong>Activation Module (v0.7.5)</strong>: Now uses the configurable activation module with weighted sigmoid confidence calculation. Supports "I prefer" and other preference patterns from activation.yaml configuration.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>message</code> (str): User message to analyze</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Dictionary containing:
- <code>should_save</code> (bool): Recommendation to save
- <code>confidence</code> (float): 0.0-1.0 confidence in recommendation
- <code>suggested_entities</code> (list[str]): Detected entities
- <code>suggested_tags</code> (list[str]): Suggested tags
- <code>suggested_strength</code> (float): Calculated importance (1.0-2.0)
- <code>reasoning</code> (str): Explanation of decision
- <code>phrase_signals</code> (dict): Detected signals for transparency</p>
<p><strong>Example:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="o">>>></span> <span class="n">result</span> <span class="o">=</span> <span class="n">analyze_message</span><span class="p">(</span><span class="s2">"I prefer PostgreSQL for databases"</span><span class="p">)</span>
<a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a><span class="o">>>></span> <span class="n">result</span><span class="p">[</span><span class="s2">"should_save"</span><span class="p">]</span>
<a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="kc">True</span>
<a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a><span class="o">>>></span> <span class="n">result</span><span class="p">[</span><span class="s2">"confidence"</span><span class="p">]</span>
<a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a><span class="mf">0.73</span>
<a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a><span class="o">>>></span> <span class="s2">"preference"</span> <span class="ow">in</span> <span class="n">result</span><span class="p">[</span><span class="s2">"reasoning"</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a><span class="kc">True</span>
</code></pre></div>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If message is invalid or None.</li>
</ul>
<hr />
<h3 id="analyze_for_recall">analyze_for_recall<a class="headerlink" href="#analyze_for_recall" title="Permanent link">¶</a></h3>
<p>Analyze a message to detect recall/search intent.</p>
<p>Returns detection signals and suggested parameters for search_memory. This tool helps the LLM decide whether to search memories based on natural language patterns like "what did I say about", "do you remember", etc.</p>
<p><strong>Decision Support (v0.6.0)</strong>: Provides confidence scores and reasoning to help Claude determine if search_memory should be called. High confidence (>0.7) suggests automatic search; medium confidence (0.4-0.7) suggests asking user first.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>message</code> (str): User message to analyze for recall patterns</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Dictionary containing:
- <code>should_search</code> (bool): Recommendation to search
- <code>confidence</code> (float): 0.0-1.0 confidence in recommendation
- <code>suggested_query</code> (str): Extracted query terms
- <code>suggested_tags</code> (list[str]): Tags to filter by (empty in Phase 1)
- <code>suggested_entities</code> (list[str]): Entities to filter by
- <code>reasoning</code> (str): Explanation of decision</p>
<p><strong>Example:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="o">>>></span> <span class="n">result</span> <span class="o">=</span> <span class="n">analyze_for_recall</span><span class="p">(</span><span class="s2">"What did I say about my API preferences?"</span><span class="p">)</span>
<a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="o">>>></span> <span class="n">result</span><span class="p">[</span><span class="s2">"should_search"</span><span class="p">]</span>
<a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a><span class="kc">True</span>
<a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="o">>>></span> <span class="n">result</span><span class="p">[</span><span class="s2">"confidence"</span><span class="p">]</span>
<a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><span class="mf">0.9</span>
<a id="__codelineno-5-6" name="__codelineno-5-6" href="#__codelineno-5-6"></a><span class="o">>>></span> <span class="n">result</span><span class="p">[</span><span class="s2">"suggested_query"</span><span class="p">]</span>
<a id="__codelineno-5-7" name="__codelineno-5-7" href="#__codelineno-5-7"></a><span class="s2">"API preferences"</span>
</code></pre></div>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If message is invalid.</li>
</ul>
<hr />
<h3 id="auto_recall_process_message">auto_recall_process_message<a class="headerlink" href="#auto_recall_process_message" title="Permanent link">¶</a></h3>
<p>Process a message and automatically recall/reinforce related memories.</p>
<p>This tool implements Phase 1 of auto-recall: silent reinforcement. When the user discusses topics, this tool:
1. Analyzes the message for topics/entities
2. Searches for related memories
3. Automatically reinforces found memories (updates last_used, use_count)
4. Returns statistics (no memory surfacing in Phase 1)</p>
<p><strong>Usage</strong>: Call this periodically during conversations to keep important memories from decaying. The LLM should call this when the user discusses substantive topics (not on simple commands/queries).</p>
<p><strong>Phase 1 Behavior</strong>: Silent mode - memories are reinforced but not surfaced to the conversation. This prevents decay while we tune surfacing strategies in later phases.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>message</code> (str): User message to analyze for recall opportunities</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Dictionary with:
- <code>success</code> (bool): Whether operation succeeded
- <code>enabled</code> (bool): Is auto-recall enabled in config?
- <code>topics_found</code> (list[str]): Topics extracted from message
- <code>memories_found</code> (int): Count of related memories
- <code>memories_reinforced</code> (list[str]): IDs of reinforced memories
- <code>mode</code> (str): Current mode: silent/subtle/interactive
- <code>message</code> (str): Human-readable summary</p>
<p><strong>Example:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a><span class="o">>>></span> <span class="n">result</span> <span class="o">=</span> <span class="n">auto_recall_process_message</span><span class="p">(</span>
<a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a><span class="o">...</span> <span class="s2">"I'm working on the STOPPER protocol implementation"</span>
<a id="__codelineno-6-3" name="__codelineno-6-3" href="#__codelineno-6-3"></a><span class="o">...</span> <span class="p">)</span>
<a id="__codelineno-6-4" name="__codelineno-6-4" href="#__codelineno-6-4"></a><span class="o">>>></span> <span class="n">result</span><span class="p">[</span><span class="s2">"memories_reinforced"</span><span class="p">]</span>
<a id="__codelineno-6-5" name="__codelineno-6-5" href="#__codelineno-6-5"></a><span class="p">[</span><span class="s2">"abc-123"</span><span class="p">,</span> <span class="s2">"def-456"</span><span class="p">]</span> <span class="c1"># IDs of STOPPER-related memories</span>
</code></pre></div>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If message is empty or invalid.</li>
</ul>
<hr />
<h2 id="graph-operations">Graph Operations<a class="headerlink" href="#graph-operations" title="Permanent link">¶</a></h2>
<h3 id="cluster_memories">cluster_memories<a class="headerlink" href="#cluster_memories" title="Permanent link">¶</a></h3>
<p>Cluster similar memories for potential consolidation or find duplicates.</p>
<p>Groups similar memories based on semantic similarity (if embeddings are enabled) or other strategies. Useful for identifying redundant memories.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>strategy</code> (str): Clustering strategy (default: "similarity").</li>
<li><code>threshold</code> (float | None): Similarity threshold for linking (0.0-1.0, uses config default if not specified).</li>
<li><code>max_cluster_size</code> (int | None): Maximum memories per cluster (1-100, uses config default if not specified).</li>
<li><code>find_duplicates</code> (bool): Find likely duplicate pairs instead of clustering.</li>
<li><code>duplicate_threshold</code> (float | None): Similarity threshold for duplicates (0.0-1.0, uses config default).</li>
</ul>
<p><strong>Returns:</strong></p>
<p>List of clusters or duplicate pairs with scores and suggested actions.</p>
<p>Dictionary with either:
- Clustering mode: <code>clusters</code> (list), <code>clusters_found</code> (int), <code>strategy</code> (str), <code>threshold</code> (float)
- Duplicate mode: <code>duplicates</code> (list), <code>duplicates_found</code> (int), <code>message</code> (str)</p>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If any input fails validation.</li>
</ul>
<hr />
<h3 id="consolidate_memories">consolidate_memories<a class="headerlink" href="#consolidate_memories" title="Permanent link">¶</a></h3>
<p>Consolidate similar memories using algorithmic merging or linking.</p>
<p>This tool handles clusters in three ways:
1. MERGE (mode="apply"): Combine memories into one (high cohesion ≥0.75)
2. LINK (mode="link"): Create 'related' relations without merging (medium cohesion 0.40-0.75)
3. PREVIEW (mode="preview"): Show what would happen without making changes</p>
<p><strong>Merging intelligently:</strong>
- Combines content (preserving unique information)
- Merges tags and entities (union)
- Calculates appropriate strength based on cohesion
- Preserves earliest created_at and latest last_used timestamps</p>
<p>Linking creates bidirectional 'related' relations to form knowledge graph connections.</p>
<p><strong>Modes:</strong>
- "preview": Generate merge preview without making changes
- "apply": Execute the consolidation/merge (requires cluster_id or auto_detect)
- "link": Create relations between cluster members without merging</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>cluster_id</code> (str | None): Specific cluster ID to act on (valid UUID, required unless auto_detect=True).</li>
<li><code>mode</code> (str): Operation mode - "preview", "apply", or "link".</li>
<li><code>auto_detect</code> (bool): If True, automatically find high-cohesion clusters.</li>
<li><code>cohesion_threshold</code> (float): Minimum cohesion for auto-detection (0.0-1.0, default: 0.75).</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Consolidation/linking preview or execution results.</p>
<p>Dictionary with:
- <code>success</code> (bool): Whether operation succeeded
- <code>mode</code> (str): Operation mode used
- <code>candidates_found</code> (int): Number of candidates (preview mode)
- <code>consolidated_clusters</code> (int): Number of clusters merged (apply mode)
- <code>linked_clusters</code> (int): Number of clusters linked (link mode)
- <code>results</code> (list): Detailed results for each operation</p>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If cluster_id is invalid or cohesion_threshold is out of range.</li>
</ul>
<hr />
<h3 id="create_relation">create_relation<a class="headerlink" href="#create_relation" title="Permanent link">¶</a></h3>
<p>Create an explicit relation between two memories.</p>
<p>Links two memories with a typed relationship.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>from_memory_id</code> (str): Source memory ID (valid UUID).</li>
<li><code>to_memory_id</code> (str): Target memory ID (valid UUID).</li>
<li><code>relation_type</code> (str): Type of relation (must be one of: related, causes, supports, contradicts, has_decision, consolidated_from).</li>
<li><code>strength</code> (float): Strength of the relation (0.0-1.0).</li>
<li><code>metadata</code> (dict[str, Any] | None): Additional metadata about the relation.</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Created relation ID and confirmation.</p>
<p>Dictionary with:
- <code>success</code> (bool): Whether operation succeeded
- <code>relation_id</code> (str): UUID of created relation
- <code>from</code> (str): Source memory ID
- <code>to</code> (str): Target memory ID
- <code>type</code> (str): Relation type
- <code>strength</code> (float): Relation strength
- <code>message</code> (str): Human-readable confirmation</p>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If any input fails validation.</li>
</ul>
<hr />
<h2 id="maintenance">Maintenance<a class="headerlink" href="#maintenance" title="Permanent link">¶</a></h2>
<h3 id="gc">gc<a class="headerlink" href="#gc" title="Permanent link">¶</a></h3>
<p>Perform garbage collection on low-scoring memories.</p>
<p>Removes or archives memories whose decay score has fallen below the forget threshold. This prevents the database from growing indefinitely with unused memories.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>dry_run</code> (bool): Preview what would be removed without actually removing.</li>
<li><code>archive_instead</code> (bool): Archive memories instead of deleting.</li>
<li><code>limit</code> (int | None): Maximum number of memories to process (1-10,000).</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Statistics about removed/archived memories.</p>
<p>Dictionary with:
- <code>success</code> (bool): Whether operation succeeded
- <code>dry_run</code> (bool): Whether this was a preview
- <code>removed_count</code> (int): Number of memories deleted
- <code>archived_count</code> (int): Number of memories archived
- <code>freed_score_sum</code> (float): Total score of affected memories
- <code>memory_ids</code> (list): Sample of affected memory IDs (first 10)
- <code>total_affected</code> (int): Total count of affected memories
- <code>message</code> (str): Human-readable summary</p>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If limit is out of valid range.</li>
</ul>
<hr />
<h3 id="promote_memory">promote_memory<a class="headerlink" href="#promote_memory" title="Permanent link">¶</a></h3>
<p>Promote high-value memories to long-term storage.</p>
<p>Memories with high scores or frequent usage are promoted to the Obsidian vault (or other long-term storage) where they become permanent.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>memory_id</code> (str | None): Specific memory ID to promote (valid UUID).</li>
<li><code>auto_detect</code> (bool): Automatically detect promotion candidates.</li>
<li><code>dry_run</code> (bool): Preview what would be promoted without promoting.</li>
<li><code>target</code> (str): Storage backend for promotion. Default: "obsidian" (Obsidian-compatible markdown). Note: This is a storage format, not a file path. Path configured via LTM_VAULT_PATH.</li>
<li><code>force</code> (bool): Force promotion even if criteria not met.</li>
</ul>
<p><strong>Returns:</strong></p>
<p>List of promoted memories and promotion statistics.</p>
<p>Dictionary with:
- <code>success</code> (bool): Whether operation succeeded
- <code>dry_run</code> (bool): Whether this was a preview
- <code>candidates_found</code> (int): Number of promotion candidates
- <code>promoted_count</code> (int): Number of memories promoted
- <code>promoted_ids</code> (list): UUIDs of promoted memories
- <code>candidates</code> (list): Details about top candidates
- <code>message</code> (str): Human-readable summary</p>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If memory_id is invalid or target is not supported.</li>
</ul>
<hr />
<h3 id="backfill_embeddings">backfill_embeddings<a class="headerlink" href="#backfill_embeddings" title="Permanent link">¶</a></h3>
<p>Generate embeddings for memories that don't have them.</p>
<p>This tool backfills embedding vectors for existing memories, enabling semantic search and improved clustering. Embeddings are generated using sentence-transformers models (default: all-MiniLM-L6-v2).</p>
<p><strong>When to use:</strong>
- After importing memories without embeddings
- When switching from text-only to semantic search
- Before running consolidation with embedding-based clustering
- To enable similarity-based features</p>
<p><strong>Safety features:</strong>
- dry_run: Preview what would be processed without making changes
- limit: Process only N memories (useful for testing or incremental backfill)
- force: Regenerate embeddings even if they exist (for model upgrades)</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><code>model</code> (str): Sentence-transformers model name (default: "all-MiniLM-L6-v2"). Common alternatives: "all-mpnet-base-v2" (higher quality, slower), "paraphrase-MiniLM-L6-v2" (good for paraphrase detection).</li>
<li><code>limit</code> (int | None): Maximum number of memories to process (1-10,000). If None, processes all.</li>
<li><code>force</code> (bool): If True, regenerate embeddings even if they exist (for model upgrades).</li>
<li><code>dry_run</code> (bool): If True, show what would be done without actually doing it.</li>
</ul>
<p><strong>Returns:</strong></p>
<p>Result dictionary with:
- <code>success</code> (bool): Whether operation completed successfully
- <code>dry_run</code> (bool): Whether this was a dry run
- <code>processed</code> (int): Number of memories processed (0 if dry_run)
- <code>errors</code> (int): Number of errors encountered
- <code>model</code> (str): Model name used
- <code>total_memories</code> (int): Total memories in database
- <code>memories_without_embeddings</code> (int): Count of memories lacking embeddings
- <code>would_process</code> (int): Number of memories that would be processed (dry_run only)
- <code>message</code> (str): Human-readable summary</p>
<p><strong>Examples:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a><span class="c1"># Preview what would be backfilled</span>
<a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a><span class="n">backfill_embeddings</span><span class="p">(</span><span class="n">dry_run</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a>
<a id="__codelineno-7-4" name="__codelineno-7-4" href="#__codelineno-7-4"></a><span class="c1"># Backfill 10 memories for testing</span>
<a id="__codelineno-7-5" name="__codelineno-7-5" href="#__codelineno-7-5"></a><span class="n">backfill_embeddings</span><span class="p">(</span><span class="n">limit</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<a id="__codelineno-7-6" name="__codelineno-7-6" href="#__codelineno-7-6"></a>
<a id="__codelineno-7-7" name="__codelineno-7-7" href="#__codelineno-7-7"></a><span class="c1"># Backfill all memories without embeddings</span>
<a id="__codelineno-7-8" name="__codelineno-7-8" href="#__codelineno-7-8"></a><span class="n">backfill_embeddings</span><span class="p">()</span>
<a id="__codelineno-7-9" name="__codelineno-7-9" href="#__codelineno-7-9"></a>
<a id="__codelineno-7-10" name="__codelineno-7-10" href="#__codelineno-7-10"></a><span class="c1"># Force regenerate with better model</span>
<a id="__codelineno-7-11" name="__codelineno-7-11" href="#__codelineno-7-11"></a><span class="n">backfill_embeddings</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"all-mpnet-base-v2"</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</code></pre></div>
<p><strong>Raises:</strong></p>
<ul>
<li><code>ValueError</code>: If limit is out of valid range.</li>
<li><code>ImportError</code>: If sentence-transformers is not installed.</li>
</ul>
<hr />
<h2 id="performance">Performance<a class="headerlink" href="#performance" title="Permanent link">¶</a></h2>
<h3 id="get_performance_metrics">get_performance_metrics<a class="headerlink" href="#get_performance_metrics" title="Permanent link">¶</a></h3>
<p>Get current performance metrics and statistics.</p>
<p><strong>Parameters:</strong> None</p>
<p><strong>Returns:</strong></p>
<p>Dictionary containing performance statistics for various operations.</p>
<p>Includes timing information, operation counts, and performance metrics for:
- Memory operations (save, search, retrieve)
- Graph operations (clustering, consolidation)
- Storage operations (read, write, compaction)</p>
<hr />
<h3 id="reset_performance_metrics">reset_performance_metrics<a class="headerlink" href="#reset_performance_metrics" title="Permanent link">¶</a></h3>
<p>Reset all performance metrics and return confirmation.</p>
<p><strong>Parameters:</strong> None</p>
<p><strong>Returns:</strong></p>
<p>Dictionary confirming metrics have been reset.</p>
<p>Dictionary with:
- <code>success</code> (bool): Always True
- <code>message</code> (str): Confirmation message</p>
<hr />
<h2 id="version-history">Version History<a class="headerlink" href="#version-history" title="Permanent link">¶</a></h2>
<ul>
<li><strong>v0.7.5</strong>: Added activation module with configurable patterns, web visualization</li>
<li><strong>v0.7.0</strong>: Added content preview and pagination features</li>
<li><strong>v0.6.0</strong>: Added natural language activation (analyze_message, analyze_for_recall, auto_recall_process_message)</li>
<li><strong>v0.5.1</strong>: Added natural spaced repetition system</li>
<li><strong>v0.5.0</strong>: Initial release with 13 core tools</li>
</ul>
<hr />
<h2 id="see-also">See Also<a class="headerlink" href="#see-also" title="Permanent link">¶</a></h2>
<ul>
<li><a href="../architecture/">Architecture Documentation</a> - System design and components</li>
<li><a href="../api/">API Quick Reference</a> - Minimal tool signatures</li>
<li><a href="../README.md#configuration">Configuration Guide</a> - Environment variables and settings</li>
<li><a href="https://github.com/simplemindedbot/cortexgraph">GitHub Repository</a> - Source code and issues</li>
</ul>
</article>
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