Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/michael-denyer/memory-mcp/recallgit clone --depth 1 https://github.com/michael-denyer/memory-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/michael-denyer/memory-mcp/recall)<a href="https://agentmods.dev/commands/michael-denyer/memory-mcp/recall"><img src="https://agentmods.dev/badge/commands/michael-denyer/memory-mcp/recall.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00004 | $0.00181 |
| Opus 5 | $0.00002 | $0.00090 |
| Sonnet 5 | $0.00001 | $0.00036 |
| Haiku 4.5 | $0.00000 | $0.00018 |
Grade A, and why
recall scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Search for memories using semantic similarity.
If a query is provided as $1, use it. Otherwise, ask the user what they want to recall.
Use the mcp__memory__recall tool with:
mode: "balanced"for general searchesmode: "precision"when user wants exact matchesmode: "exploratory"when user wants broad resultsexpand_relations: trueto include related memories via knowledge graph
Present results clearly showing:
- Memory content
- Confidence level (high/medium/low)
- Memory type and tags
- Related memories if expanded
If no results found, suggest:
- Trying a different query
- Using
/memory-mcp:listto browse all memories - Checking if the topic was ever stored
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 26 lines · 4 tokens per session scan A 50833e4bed7b
recall is a command published in the GitHub repository michael-denyer/memory-mcp (7 stars, last pushed 15d ago), licensed MIT. It adds 4 tokens to every session and 181 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
neo-review
Get Neo's code review with semantic matching against past findings in memory. Use on a diff or module before merge, especially where earlier mistakes in this codebase are likely to recur. Skip for formatting, lint-catchable issues, and single-line changes.