Codebase Memory MCP vs Tempera — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Codebase Memory MCP vs Tempera
In-depth architectural comparison of the Codebase Memory MCP and Tempera MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Codebase Memory MCP
Knowledge & Memory · Local stdio
Quality: 73/100 (Great) | Auth: No auth required
Tempera
Knowledge & Memory · Local stdio
Quality: 45/100 (Fair) | Auth: No auth required
Verdict Summary: Choose Codebase Memory MCP if you need specialized Knowledge & Memory tools running via a local process. Choose Tempera if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Codebase Memory MCP when:
You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Code-intelligence engine that indexes a repo into a persistent knowledge graph — functions, classes, call chains, HTTP routes, cross-service links. 159 languages via tree-sitter + Hybrid LSP, sub-ms structural queries, 99% fewer tokens than grep. Single static binary, zero dependencies, 100% local. npx codebase-memory-mcp
Persistent episodic memory for AI coding: capture, retrieve, brief, dream cycle, cross-project.
Index a repository. full/moderate add semantics; fast omits them; cross-repo-intelligence links services. Reports coverage gaps.
search_graph
Find symbols via BM25 query, regex name/qn filters, or semantic_query. Rows keep qn/file/lines and in/out over CALLS/USAGE/CALL_REFERENCE/INHERITS/IMPLEMENTS.
query_graph
Read-only Cypher for multi-hop, aggregation, complexity, or cross-service analysis. Default: 200 visible rows with exact/lower-bound totals and truncation; continue safely with next_cursor. graph=missed is a file tree of flagged coverage gaps; absence is not proof of completeness. Use get_graph_schema(diagnostics=full) for properties.
trace_path
Trace callers/callees, data flow, or cross-service paths. Defaults exclude tests and resolver evidence. Rows keep qn/hop with explicit totals, relations, and continuations.
get_code_snippet
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Codebase Memory MCP is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Tempera belongs to Knowledge & Memory using local stdio subprocess. Select Codebase Memory MCP when you need capabilities focused on knowledge & memory and Tempera when you require tools for knowledge & memory.
Read a search_graph symbol. auto bounds source and outlines large containers; full restores up to 500 lines. Source/outline pages continue; coverage_note marks gaps.
get_file_outline
Declaration outline of one exact repository-relative file: optional exact label filter, source order, exact total/offset/limit paging; file/folder/container nodes excluded.
get_graph_schema
Get node-label and edge-type counts. diagnostics=full also lists queryable properties.
compare_graphs
Compare two indexed snapshots: deterministic target-only additions and base-only removals of stable node/edge identities; each set capped by limit and a 512 KiB budget with exact totals and truncation reasons.
Graph-ranked text search: compact symbols, full bounded source, or file paths.
list_projects
List projects with stable paging. Identity is lean; stats adds graph sizes.
delete_project
Delete a project from the index
+5 more tools listed on main page
Tempera Tools (12)
tempera_session_start
Call ONCE at the very start. Returns any clarifying question tempera drafted after a previous failed/partial session in this project.
tempera_brief
Call once the file set is known. Joins pending ask-back, reasoning template, top correction categories for these files, should-have-asked triggers, and calibration warning into one response. Pass `task_type` + `domain` for richer output. Set `cross_project=true` to supplement with rows from other p…
tempera_retrieve
Search for similar past episodes. Set `scope="cross-project"` to include transferable claims from other projects.
tempera_template
Pull the reasoning template stored for a `(task_type, domain)` pair. The step sequence past wins followed.
tempera_log_correction
When the user corrects an assumption / decision / piece of code. Categorized log; the brief surface uses it.
tempera_log_should_have_asked
When you realize mid-task you should have asked a question up front. Records the trigger context, the question, and the eventual answer.
tempera_capture
Save session as an episode. Auto-detects session links and runs propagation. The intent-extraction LLM call also suggests a `ValidityScope` for cross-project routing.
tempera_feedback
Mark retrieved episodes as helpful or not. Drives the utility-learning loop.
tempera_status
Per-project memory health snapshot.
tempera_stats
Statistics + trend analytics (helpfulness over time, domain growth, learning curve).
tempera_propagate
Multi-hop Bellman propagation with convergence tracking. Periodic maintenance.
tempera_review
Consolidate similar BKMs, cleanup. Run after related task series.