DevRev MCP Server
Enables importing data from Salesforce into DevRev for search and retrieval via the DevRev APIs.
Enables importing data from Zendesk into DevRev for search and retrieval via the DevRev APIs.
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., "@DevRev MCP Serversearch for tickets assigned to me this week"
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.
DevRev MCP server
Overview
A Model Context Protocol server for DevRev. It is used to search and retrieve information using the DevRev APIs.
Related MCP server: Scrapeless MCP Server
Tools
search: Search for information using the DevRev search API with the provided query and namespace.get_object: Get all information about a DevRev object using its ID.
Configuration
Get the DevRev API key
Go to https://app.devrev.ai/signup and create an account.
Import your data from your existing data sources like Salesforce, Zendesk while following the instructions here.
Generate an access token while following the instructions here.
Usage with Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
"mcpServers": {
"devrev": {
"command": "uvx",
"args": [
"devrev-mcp"
],
"env": {
"DEVREV_API_KEY": "YOUR_DEVREV_API_KEY"
}
}
}"mcpServers": {
"devrev": {
"command": "uv",
"args": [
"--directory",
"Path to src/devrev_mcp directory",
"run",
"devrev-mcp"
],
"env": {
"DEVREV_API_KEY": "YOUR_DEVREV_API_KEY"
}
}
}Available Tools
2 toolsget_objectB
Get all information about a DevRev object using its ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states this is a read operation ('Get'), but doesn't disclose behavioral traits like authentication needs, rate limits, error handling, or what 'all information' entails. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 front-loads the core purpose without any wasted words. It's appropriately sized for a simple tool, making it easy to parse and understand quickly.
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 simplicity (1 parameter, no output schema, no annotations), the description is minimal. It states the basic action but lacks details on return values, error cases, or operational context. Without annotations or output schema, the description should provide more completeness for effective use, but it falls short.
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 0%, so the description must compensate. It adds meaning by specifying that the 'id' parameter is for a 'DevRev object,' which clarifies the context beyond the schema's generic string type. However, it doesn't detail format, constraints, or examples, leaving some ambiguity. With one parameter, the baseline is higher, but incomplete coverage limits the score.
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 ('DevRev object') with the specific mechanism ('using its ID'). It distinguishes from the sibling 'search' tool by focusing on retrieval by ID rather than searching. However, it doesn't specify what type of information is returned beyond 'all information,' which keeps it from 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?
The description implies usage when you have a specific object ID, contrasting with the 'search' sibling for broader queries. However, it lacks explicit guidance on when to use this versus alternatives, prerequisites, or exclusions, leaving usage context somewhat inferred rather than clearly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchC
Search DevRev using the provided query
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| namespace | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool performs a search but doesn't disclose behavioral traits such as whether it's read-only, destructive, requires authentication, has rate limits, or what the output format might be. The description is minimal and lacks essential operational context.
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 zero waste. It's appropriately sized for a simple tool and front-loaded with the core action. Every word earns its place, 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 complexity (2 parameters with 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain parameter usage, behavioral aspects, or what to expect from results. For a search tool with structured inputs, more context is needed to be adequately helpful.
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 0%, so the description must compensate. It mentions 'using the provided query' but doesn't explain the 'query' parameter's semantics or the 'namespace' parameter with its enum values (article, issue, ticket). The description adds minimal meaning beyond the bare schema.
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 action ('search') and target ('DevRev'), which provides a basic purpose. However, it's vague about what exactly is being searched (e.g., content, records, objects) and doesn't distinguish from the sibling tool 'get_object'. It avoids tautology by not just repeating the name/title.
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 'get_object'. It mentions 'using the provided query' but doesn't specify contexts, prerequisites, or exclusions. Usage is implied only through the tool name and parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
v1.0.0- Added
get_object - Added
search
TDQS
The two tools have clearly distinct purposes: get_object retrieves a specific object by ID, while search performs a broader query-based lookup. There is no overlap or ambiguity between these operations.
Both tools follow a consistent verb-based naming pattern (get_ and search), with clear, descriptive names that align well with their functions. No deviations or mixed conventions are present.
With only two tools, the server feels severely under-scoped for a DevRev platform, which typically involves objects like issues, tickets, or users. This minimal set lacks essential CRUD operations (e.g., create, update, delete) and limits agent functionality.
The tool surface is severely incomplete for a DevRev domain, offering only retrieval and search capabilities. There are no tools for creating, updating, or deleting objects, which are fundamental operations in such systems, leading to significant gaps in agent workflows.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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