Skip to main content
Glama
topoteretes

cognee-mcp

by topoteretes
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

recall

Find answers from AI memory using natural language queries, with automatic routing that checks the session cache first, then falls back to the knowledge graph.

Instructions

Search memory with auto-routing and session awareness.

When session_id is provided without datasets or search_type, searches session cache first by keyword matching. Falls through to the permanent knowledge graph if no session results match.

Auto-routing picks the best search strategy when search_type is not specified.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query to search for.
top_kNoMaximum results to return (default: 10).
datasetsNoComma-separated dataset names to search within.
session_idNoSession ID for session-first search.
search_typeNoOverride auto-routing. Options: GRAPH_COMPLETION, GRAPH_COMPLETION_COT, RAG_COMPLETION, CHUNKS, SUMMARIES, TEMPORAL, FEELING_LUCKY, etc.
system_promptNoOverride the synthesis prompt for completion searches. When omitted, falls back to COGNEE_MCP_RECALL_SYSTEM_PROMPT / _FILE if configured on the server.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed15 schema fields changedv1.5.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / datasets / description
      Added value: +"Comma-separated dataset names to search within."
    • removedInput schema / properties / datasets / title
      Removed value: -"Datasets"
    • addedInput schema / properties / query / description
      Added value: +"Natural language query to search for."
    • removedInput schema / properties / query / title
      Removed value: -"Query"
    • addedInput schema / properties / search_type / description
      Added value: +"Override auto-routing. Options: GRAPH_COMPLETION,\nGRAPH_COMPLETION_COT, RAG_COMPLETION, CHUNKS, SUMMARIES,\nTEMPORAL, FEELING_LUCKY, etc."
    • removedInput schema / properties / search_type / title
      Removed value: -"Search Type"
    • addedInput schema / properties / session_id / description
      Added value: +"Session ID for session-first search."
    • removedInput schema / properties / session_id / title
      Removed value: -"Session Id"
    • addedInput schema / properties / system_prompt / description
      Added value: +"Override the synthesis prompt for completion searches. When omitted,\nfalls back to COGNEE_MCP_RECALL_SYSTEM_PROMPT / _FILE if configured\non the server."
    • removedInput schema / properties / system_prompt / title
      Removed value: -"System Prompt"
    • addedInput schema / properties / top_k / description
      Added value: +"Maximum results to return (default: 10)."
    • removedInput schema / properties / top_k / title
      Removed value: -"Top K"
    • removedInput schema / title
      Removed value: -"recallArguments"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "items": {},
+      "type": "array"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "type": "object",
+  "x-fastmcp-wrap-result": true
+}
  2. Addedv1.4.1
  3. Removedv1.4.0
  4. Changed1 schema field changedv1.0.2
    • changedInput schema / properties / top_k / default
      Previous value: -10New value: +15
  5. First observedv1.0.1

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations present, the description carries the disclosure burden and it does reveal non-obvious behavior: session-first keyword matching, fallback to the permanent knowledge graph, and automatic search-strategy selection. It does not discuss side effects, permissions, or rate limits, but for a search action the main routing behavior is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is tight and front-loaded, with the main purpose in the first sentence and exactly two supporting details about routing/fallback. No filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and thorough parameter documentation, the description covers the non-obvious routing behavior needed to understand the tool. It lacks explicit guidance about edge cases or alternatives, but the combination of description plus schema is adequate for confident invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema coverage is 100%, so the schema already documents all six parameters; the tool description adds useful conditional context around session_id, datasets, and search_type but does not explain query, top_k, dataset syntax, or system_prompt beyond the schema. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ('Search memory') and adds distinguishing behavioral qualifiers ('auto-routing and session awareness'), which separates it from the write/remove siblings (remember, forget). Subsequent sentences clarify the scope with session-cache and knowledge-graph behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear invocation context: when session_id is supplied without datasets/search_type it searches the session cache first and falls through to the knowledge graph, and auto-routing applies when search_type is omitted. It does not explicitly name alternative tools or exclusion scenarios, but the parameter-condition guidance is sufficient for most recall usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/topoteretes/cognee'

If you have feedback or need assistance with the MCP directory API, please join our Discord server