Enables querying Snapshot.org, a decentralized governance platform on Ethereum, providing tools for accessing proposal data, spaces, and user information from Snapshot voting interfaces.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Snapshot Server show me the top 5 ranked Snapshot spaces"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol server for interacting with Snapshot.org. This server provides MCP-compliant tools for querying Snapshot spaces, proposals, and users.
npm install mcp-snapshot-serverRelated MCP server: TON Blockchain MCP
In your Claude Desktop config file (located at ~/Library/Application Support/Claude/claude_desktop_config.json on Mac or %APPDATA%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": {
"snapshot": {
"command": "node",
"args": [
"/ABSOLUTE/PATH/TO/snapshot-server/build/index.js"
]
}
}
}Get a list of Snapshot spaces
limit: Number of spaces to fetch (optional)
skip: Number of spaces to skip (optional)
Get a ranked list of Snapshot spaces with detailed information
first: Number of spaces to fetch (default: 18)
skip: Number of spaces to skip (default: 0)
category: Category to filter by (default: 'all')
search: Search term to filter spaces (optional)
Get proposals for a specific space
spaceId: ID of the space
state: Filter by proposal state (active, closed, pending, all)
limit: Number of proposals to fetch
Get details of a specific proposal
proposalId: ID of the proposal
Get information about a Snapshot user
address: Ethereum address of the user
Clone the repository:
git clone https://github.com/crazyrabbitLTC/mcp-snapshot-server.gitInstall dependencies:
npm installBuild the project:
npm run buildMIT © Dennison Bertram
Get details of a specific proposal
| Name | Required | Description | Default |
|---|---|---|---|
| proposalId | Yes | ID of the proposal |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states a read operation ('Get'), implying it's likely safe and non-destructive, but doesn't confirm this or add context like authentication needs, rate limits, error handling, or return format. The description is minimal and lacks behavioral details beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words, making it appropriately concise. It's front-loaded with the core action, though it could be more structured if it included additional context. Efficiency is high, but it borders on under-specification.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what details are returned, error conditions, or behavioral traits. For a tool with minimal structured data, the description should provide more context to aid the agent in correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'proposalId' fully documented in the schema. The description adds no additional meaning about the parameter beyond what the schema provides (e.g., format examples or constraints). Baseline 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the verb ('Get') and resource ('details of a specific proposal'), which provides a basic understanding of purpose. However, it lacks specificity about what details are retrieved and doesn't differentiate from sibling tools like 'getProposals' (which presumably lists multiple proposals). The description is functional but vague about scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a proposal ID), exclusions, or comparisons to siblings like 'getProposals' for listing or 'getUser' for user-related data. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get proposals for a Snapshot space
| Name | Required | Description | Default |
|---|---|---|---|
| spaceId | Yes | ID of the space | |
| state | No | Filter by proposal state (active, closed, pending, all) | |
| limit | No | Number of proposals to fetch |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Get proposals' but doesn't specify whether this is a read-only operation, if it requires authentication, how results are returned (e.g., pagination), or any rate limits. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'proposals' entail in this context, how results are structured, or any behavioral traits like error handling. For a tool with 3 parameters and no structured support, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents all parameters (spaceId, state, limit) with clear descriptions. The description adds no additional meaning beyond what the schema provides, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('proposals for a Snapshot space'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'getProposal' (singular vs. plural) or 'getSpaces' (proposals vs. spaces), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'getProposal' (for a single proposal) or 'getSpaces' (for listing spaces). The description only states what it does without context about usage scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get ranked list of Snapshot spaces with detailed information
| Name | Required | Description | Default |
|---|---|---|---|
| first | No | Number of spaces to fetch (default: 18) | |
| skip | No | Number of spaces to skip (default: 0) | |
| category | No | Category to filter by (default: 'all') | |
| search | No | Search term to filter spaces |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves a 'ranked list' and 'detailed information', but doesn't explain what 'ranked' means (e.g., by popularity, activity), the format of the detailed information, or any limitations like rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage guidelines, and output format, which are needed for full contextual understanding, especially without annotations or output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all four parameters with defaults and purposes. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage without compensating further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get ranked list') and resource ('Snapshot spaces with detailed information'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'getSpaces' or 'getProposals', which likely retrieve similar data, so it doesn't achieve full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'getSpaces' or 'getProposals'. There's no mention of specific contexts, prerequisites, or exclusions, leaving the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get list of Snapshot spaces
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of spaces to fetch | |
| skip | No | Number of spaces to skip |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation, implying read-only behavior, but doesn't mention authentication requirements, rate limits, pagination behavior, or what the returned list structure looks like. This leaves significant gaps for a list-fetching tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states exactly what the tool does with zero wasted words. It's appropriately sized for a simple list-fetching tool and gets straight to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list-fetching tool with no annotations and no output schema, the description is inadequate. It doesn't explain what a 'Snapshot space' is, what fields are returned, how pagination works (despite having limit/skip parameters), or any error conditions. The agent would need to guess about the return format and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with both 'limit' and 'skip' clearly documented in the schema. The description adds no additional parameter information beyond what's already in the structured schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('list of Snapshot spaces'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'getRankedSpaces' or 'getProposals', but it's specific enough to understand what this tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'getRankedSpaces' or 'getProposals'. There's no mention of context, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get information about a Snapshot user
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Ethereum address of the user |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'Get information' but doesn't specify what type of information, whether it's read-only, requires authentication, has rate limits, or error handling. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's appropriately sized and front-loaded, efficiently conveying the core purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what information is returned, potential errors, or behavioral traits, making it inadequate for a tool that retrieves user data with a required parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'address' parameter documented as 'Ethereum address of the user'. The description adds no additional meaning beyond this, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('information about a Snapshot user'), making the purpose understandable. However, it doesn't differentiate this from potential sibling tools like 'getSpaces' or 'getProposals' beyond specifying the user resource, missing explicit distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, such as needing a valid Ethereum address, or comparisons to other user-related tools, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0getProposalgetProposalsgetRankedSpacesgetSpacesgetUserEach tool has a clearly distinct purpose targeting different Snapshot resources: proposals (specific vs. list), spaces (ranked vs. list), and users. No overlap exists between these categories, making tool selection straightforward for an agent.
All tools follow a consistent verb_noun naming pattern with 'get' as the verb and descriptive nouns (Proposal, Proposals, RankedSpaces, Spaces, User). The naming is uniform and predictable across the entire set.
With 5 tools, this server is well-scoped for interacting with Snapshot's core entities (proposals, spaces, users). Each tool serves a distinct and necessary function without bloat, making the count appropriate for the domain.
The toolset covers essential read operations for proposals, spaces, and users, which aligns with Snapshot's governance and voting platform. A minor gap exists in write operations (e.g., creating proposals or voting), but agents can still perform core informational tasks effectively.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
A Model Context Protocol server for Wix AI tools
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/crazyrabbitLTC/mcp-snapshot-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
