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 instructions/codester1000/raindrop-mcp/mcp-refactorgit clone --depth 1 https://github.com/codester1000/raindrop-mcpWhat 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 | $0.00719 | $0.00719 |
| Opus 5 | $0.00360 | $0.00360 |
| Sonnet 5 | $0.00144 | $0.00144 |
| Haiku 4.5 | $0.00072 | $0.00072 |
Grade A, and why
raindrop-mcp mcp-refactor.instructions.md 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 2d 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.
This is a copy
100% identical to raindrop-mcp mcp-refactor.instructions.md — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
- Capabilities: Resources, Sampling, Elicitation a. Resources
Expose all major Raindrop entities as MCP resources: collections://all, collections://{id}, bookmarks://{id}, tags://all, highlights://all, user://info, etc. Implement resource discovery and navigation (list, get, search, children, etc.) using standard MCP resource URIs and methods. Ensure each resource supports GET (read), and where appropriate, CREATE, UPDATE, DELETE (write) actions. b. Sampling
For large collections/bookmarks/tags/highlights, implement sampling endpoints: e.g., bookmarks://collection/{id}?sample=10 returns a random or recent sample. Add tool parameters for limit, offset, and sample to all list/search tools. Use MCP’s sampling capability to advertise this in the server manifest. c. Elicitation
Implement elicitation tools for: Confirming destructive actions (delete, merge, etc.) Requesting missing parameters (e.g., if a required field is omitted, prompt the LLM/user) Use the MCP elicitation capability to allow the server to ask clarifying questions or confirmations. 2. Streamlining Tools for LLMs a. Hierarchical, Predictable Naming
Use a consistent {resource}_{action} pattern (e.g., collection_list, bookmark_create, tag_manage). Group related actions under a single tool with an operation parameter where possible (e.g., collection_manage for create, update, delete). b. Reduce Redundancy
Collapse similar tools: Merge collection_create, collection_update, collection_delete into collection_manage with an operation parameter. Do the same for bookmarks, tags, highlights. For read-only actions, keep list, get, and search as separate, simple tools. c. LLM-Friendly Descriptions
Ensure every tool and parameter has a clear, concise description. Use Zod schemas for validation and documentation. 3. Example: Refactored Tool Set Tool Name Description Operations/Params collection_manage Create, update, or delete a collection operation: create/update/delete collection_list List all or child collections parentId bookmark_manage Create, update, delete, move, tag bookmarks operation, ids, data bookmark_search Search bookmarks with filters query, tags, collection, etc. tag_manage Rename, merge, delete tags operation, tagNames, newName highlight_manage Create, update, delete highlights operation, id, data user_profile Get user info user_statistics Get user or collection stats collectionId import_export Import/export bookmarks, check status operation, format, etc. diagnostics Server diagnostics includeEnvironment 4. LLM/AI-Optimized Features Resource URIs: Support direct resource access via URIs (e.g., collections://all). Streaming: For large lists, support streaming or pagination. Sampling: Add sample and limit parameters to all list/search tools. Elicitation: Use MCP’s elicitation to prompt for missing/ambiguous info and confirmations. Consistent Error Handling: Always return structured, descriptive errors. 5. Next Steps Refactor tool initializers to group actions and reduce tool count. Ensure all tools/resources are discoverable and documented in the manifest. Advertise resources, sampling, and elicitation in the MCP server capabilities. Add/expand tests to cover new tool structure and resource URIs. Would you like a concrete code refactor example for one of these tool groups, or a manifest/capabilities update?
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.
- 2d ago First seen · 59 lines · 719 tokens per session scan A 32a60797b84e
raindrop-mcp mcp-refactor.instructions.md is an instructions file published in the GitHub repository codester1000/raindrop-mcp (0 stars, last pushed 8mo ago), licensed MIT. It adds 719 tokens to every session, about $0.0036 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to raindrop-mcp mcp-refactor.instructions.md, differing in 0 lines, and is treated as a copy.
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