Aman Amith Shastry Scientific Computation Mcp vs Keymem
In-depth architectural comparison of the Aman Amith Shastry Scientific Computation Mcp and Keymem 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
Aman Amith Shastry Scientific Computation Mcp
Workplace & Productivity · Local stdio
Quality: 48/100 (Fair) | Auth: No auth required
Keymem
Workplace & Productivity · Local stdio
Quality: 65/100 (Great) | Auth: No auth required
Verdict Summary: Choose Aman Amith Shastry Scientific Computation Mcp if you need specialized Workplace & Productivity tools running via a local process. Choose Keymem if your workspace requires Workplace & Productivity integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Aman Amith Shastry Scientific Computation Mcp when:
You need dedicated capabilities in the Workplace & Productivity domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Aman Amith Shastry Scientific Computation Mcp Tools (28)
create_tensor
view_tensor
delete_tensor
add_matrices
subtract_matrices
multiply_matrices
scale_matrix
matrix_inverse
transpose
determinant
rank
compute_eigen
+16 more tools listed on main page
Keymem Tools (12)
recall
Search long-term memory for what is already known about the user, project, or topic — call this before your first reply and whenever the topic shifts. Always pass the active namespace when known. Returns matching key clusters only (not memory content): canonical concept, aliases, key type, match score, linked-memory count, hub status, and specificity. For a core lookup, complete read_key(key_id, original_query, namespace) then read_memory(memory_id, via_key_id, namespace): only the full read grows depth/access, reinforces the traversed edge, and learns aliases. Use short focused noun queries and decompose multi-fact questions into several recall calls. inject:true is only an unconfirmed passive preview and must not replace that traversal; injected memories are not reinforced. inject_top_k defaults to 1; inject_max_chars defaults to 2000 and marks truncated previews. An empty result returns {status:'no_match', nearest_keys} — the closest stored concepts below the gate; retry with one of those concepts when relevant.
browse_keys
Browse the vocabulary of one namespace when recall has no hit or you need an entry point. Returns active key clusters with hubs first, then by linked-memory count. This is index metadata only; continue with read_key(key_id, query, namespace) and read_memory(memory_id, via_key_id, namespace).
read_key
List the memories stored under one key (concept), ranked. Returns the canonical key, its aliases, and hub metadata plus ranked memory IDs and metadata — never memory content. Always pass the original focused query and active namespace when known: handles are then ranked by content relevance, which is essential for hubs. Call read_memory(memory_id, via_key_id=key_id, namespace) on the selected handle to confirm the fact and reinforce the path. Use limit/offset to page without flooding context.
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).
Aman Amith Shastry Scientific Computation Mcp Configuration
Aman Amith Shastry Scientific Computation Mcp is categorized under Workplace & Productivity and uses a local stdio subprocess. In contrast, Keymem belongs to Workplace & Productivity using local stdio subprocess. Select Aman Amith Shastry Scientific Computation Mcp when you need capabilities focused on workplace & productivity and Keymem when you require tools for workplace & productivity.
Read the full content of one stored memory (selected via read_key). Returns the memory and all connected key clusters so exploration can continue Key → Memory → Key. Pass via_key_id from the selected key: only that traversed edge is Hebbian-reinforced, and depth/access count increase only when this full read occurs.
remember
MANDATORY END-OF-TURN GATE: before replying, save every durable fact newly revealed this turn (names, preferences, decisions, corrections, project facts, goals). A durable fact left unsaved is a bug; save silently in the same turn. Save nothing only after consciously confirming that nothing durable appeared. Before writing, recall() the topic in the same namespace and reuse returned canonical concepts or aliases. Use 3-6 diverse ATOMIC concept keys of 1-2 words each, never memory-specific phrases (use 'Nexora' and 'portfolio', not 'Nexora portfolio'); 3+-word keys are flagged in hints.phrase_keys and are measurably 91% unreachable singletons. CROSS-LINGUAL: register both language forms together (for example '포트폴리오' and 'portfolio'). Shared broad keys become navigable hubs. namespace groups memories by project/context; ttl_seconds sets expiry; related_to adds explicit memory links; source attaches provenance and is auto-stamped with the server session, a timestamp, and — when a host agent (Claude Code, Codex) transcript is active — host_session/host_agent/host_turn so the memory can be traced back to its original conversation via get_conversation. The response may include hints.near_keys (existing concepts your keys nearly duplicate — prefer reusing those concepts) and hints.language_note (add the missing-language variants).
correct
Update outdated information. Use when user corrects you or info changes (e.g. moved cities, changed job). Old version is preserved but weakened — never lost. Omit keys to keep the same search terms. related_to links the updated memory to other memory IDs.
related
Find other memories associated with a memory you already have (by ID). Returns neighboring memories connected by shared keys or explicit links. For normal agent-driven navigation prefer read_memory(), inspect its returned keys, then call read_key().
forget
Permanently delete a memory. Only use for completely wrong information. For outdated info, use correct() instead — it preserves history.
list_memories
List all stored memories. namespace filters by project/context. Expired memories are excluded. Prefer recall() for normal retrieval.
remember_batch
MANDATORY END-OF-TURN GATE: when a turn reveals multiple durable facts, save them silently before replying. A durable fact left unsaved is a bug. Recall each topic first, reuse canonical concept-level keys (ATOMIC, 1-2 words each — never phrases), and register cross-lingual forms together. Each item: {content, keys, key_types?, namespace?, ttl_seconds?, related_to?}. Returns saved IDs and is more efficient than multiple remember() calls.
cleanup_expired
Delete all memories past their ttl. Returns count of deleted memories. Call periodically to keep memory clean.
memory_stats
Get counts of keys, memories, and links in the system.