raindrop-mcp mcp-refactor.instructions.md

raindrop-mcp mcp-refactor.instructions.md is an instructions file for GitHub Copilot from adeze/raindrop-mcp. It costs 719 tokens per session, scanned A, original, MIT.

A redesign plan for Raindrop’s MCP tools, an interface that lets AI agents read and change Raindrop data such as bookmarks, collections, tags, and highlights.

In plain words
What is it for?
It is for organizing MCP resources, adding predictable names for actions, returning selected or recent items, and asking for missing details or confirmation before destructive changes.
Why use it?
It makes the available data and actions easier for an AI agent to discover and use. It also supports samples, limits, and confirmation questions so large results and risky changes are easier to handle.

Instructions file for GitHub Copilot

Install

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.

agentmods
npx agentmods add instructions/adeze/raindrop-mcp/mcp-refactor
Clone the repo
git clone --depth 1 https://github.com/adeze/raindrop-mcp

Made for: GitHub Copilot.

Wrote 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.

agentmods badge for raindrop-mcp mcp-refactor.instructions.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/adeze/raindrop-mcp/mcp-refactor.svg)](https://agentmods.dev/instructions/adeze/raindrop-mcp/mcp-refactor)
Your own site
<a href="https://agentmods.dev/instructions/adeze/raindrop-mcp/mcp-refactor"><img src="https://agentmods.dev/badge/instructions/adeze/raindrop-mcp/mcp-refactor.svg" alt="Measured on agentmods" height="20"></a>
Per session 719 This file is loaded in full into every session.
When invoked 719 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash 32a60797b84e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.github/instructions/mcp-refactor.instructions.md · 59 lines

What it actually says

  1. 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?

Changes

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.

  1. 4d ago First seen · 59 lines · 719 tokens per session scan A 32a60797b84e

Subscribe to this mod's changes

raindrop-mcp mcp-refactor.instructions.md is an instructions file published in the GitHub repository adeze/raindrop-mcp (180 stars, last pushed 1mo 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.