Camber MCP Server
Server Details
An agentic AI for data science. Agents that understand your docs, your processes, your way of working. Expertise, exactly when you need it. @mention any agent to bring specialized help directly into your conversation. Native connectors to Snowflake, Databricks, BigQuery, S3, Google Drive. Camber Stash for team collaboration. Unlimited CPU/GPU on demand.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.4/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: chatting with an agent, listing agents, and providing usage instructions. There is no overlap or ambiguity between them.
Two tools follow the 'agents_<action>' pattern (agents_chat, agents_list), but 'usage_guide' breaks this pattern. Overall consistent snake_case but lacking full uniformity.
With only 3 tools for the domain of Camber agent interaction, the set is minimal. While it covers basic needs (chat, list, guide), it feels slightly thin for a full agent management server.
The tools cover chatting with and listing agents, but lack operations like agent creation, deletion, or stash file management. The usage guide partially compensates by pointing to CLI commands, but notable gaps remain.
Available Tools
3 toolsagents_chatAInspect
Chat with a Camber agent with the specified tag and message.
Returns: The agent's reply text. The run may reference **output files in Camber Stash**
(`stash://...` paths). Those files live in the cloud until the user copies them locally.
**After the call — sync stash outputs (user's terminal, Camber CLI):**
1. Note every `stash://user/...` path the agent produced or mentioned.
2. Download to the user's machine, e.g.
`camber stash cp stash://<user>/<path/to/file> ./<local_filename>`
(use a directory path for `./` if copying a folder; see `camber stash cp --help`).
3. To browse first: `camber stash ls stash://<user>/...`
4. Confirm with the user where files were saved.
Docs: https://docs.cambercloud.com/docs/stash/ and
https://docs.cambercloud.com/docs/camber-cli/stash| Name | Required | Description | Default |
|---|---|---|---|
| message | No | The message to send with the agent. | |
| model_id | No | The name of the model to use for the agent. For example, Haiku, Sonnet | claude-sonnet-4-6 |
| agent_tag | No | Tag of the agent you want to chat with. Start with @ to use the agent's tag.agent_tag = `@owner_name.agent_alias`For example, @camber_user.ai-agent-1 | |
| conversation_id | No | Optional UUID of an existing conversation to continue. Pass the conversation_id returned by a previous agents_chat call to maintain context across multiple turns. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden. It discloses that output files may be in Camber Stash and live in the cloud until copied, and provides detailed sync steps. It also states the return value. No contradictions.
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 well-structured with a clear opening sentence followed by a numbered list for actions. It is relatively concise but includes necessary details. Could be slightly tighter but effective.
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 and presence of output schema, the description is complete. It covers return value, stash behavior, sync steps, and provides documentation links. No gaps.
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%, so baseline is 3. The description does not add meaningful parameter details beyond the schema; it merely restates 'tag and message'. No extra semantic value added for parameters.
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 'Chat with a Camber agent with the specified tag and message', using a specific verb and resource. It distinguishes well from siblings like 'agents_list' (listing agents) and 'usage_guide' (guide).
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 explicit post-call steps for syncing stash outputs, which guides usage. However, it does not explicitly state when to use this tool versus alternatives, though the sibling context makes it obvious. Slightly lacking direct when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
agents_listAInspect
List Camber agents with optional scope and pagination.
Returns: AgentListInfo object containing:
- data: List of AgentInfo objects
- page: PageInfo object with pagination details (page, size, total, num_page)| Name | Required | Description | Default |
|---|---|---|---|
| page | No | The page number to list agents from (default: 1). | |
| size | No | The number of agents to list per page (default: 20). | |
| scope | No | Scope of agents to list. Use 'community' for community agents, 'system' for system agents, 'team' for a specific team, or leave empty (null) to list your personal agents. | |
| team_name | No | Unique name of the team whose agents you want to list. Required when scope is 'team'. Ignored for other scopes. |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| page | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the burden of behavioral disclosure. It describes the return structure (AgentListInfo with data and page) but does not mention potential side effects or permissions. Since it is a read-only list operation, the lack of destructive hints is acceptable.
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 concise and front-loaded. Two sentences: first states the purpose, second details the return object. It is efficient without unnecessary fluff.
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 complexity (4 parameters, no required params) and the presence of an output schema, the description adequately covers the tool's behavior. It includes return type details. Sibling tools are clearly different, so no confusion.
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 coverage is 100%, so the baseline is 3. The description adds context beyond the schema: it explains the scope values ('community', 'system', 'team', null) and clarifies that team_name is required when scope is 'team'. This adds meaningful usage guidance.
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 'List Camber agents with optional scope and pagination.' It specifies the verb (list) and resource (agents), and distinguishes from siblings by not mentioning chat or usage guide functionalities.
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 through the context of listing agents with scope and pagination, but does not explicitly state when to use this tool versus alternatives like agents_chat. However, the purpose is clear enough for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
usage_guideAInspect
Instructions for correctly using this MCP server. Call at the start of a session to learn tools, auth, CLI prerequisites, and stash file handling.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the tool as informational ('learn'), implying no side effects. With no annotations, the description adequately conveys safe read 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?
Two sentences, front-loaded with purpose, no redundant words. Efficient and clear.
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 zero parameters, simple intent, and a clear list of output content, the description is fully sufficient for an agent to use this tool correctly.
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?
No parameters; schema coverage is 100%. Description naturally does not need to add parameter info.
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?
Clearly states it provides instructions for using the MCP server, listing specific topics (tools, auth, CLI prerequisites, stash file handling). No ambiguity with siblings.
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?
Explicitly says 'Call at the start of a session', providing clear usage context. Does not list alternatives, but siblings are distinct in purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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The server is experiencing an outage
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