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., "@BeeBoo MCP Server request approval to deploy the new database schema"
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.
Model Context Protocol (MCP) server for BeeBoo — Human-in-the-Loop Infrastructure for AI Agents.
This server enables AI agents like Claude, Cursor, and Windsurf to natively interact with BeeBoo's capabilities:
Knowledge Base — Search, add, and list knowledge entries
Approvals — Request and check human approval status
Work Requests — Create and track work requests
Get your BeeBoo API key from beeboo.ai/settings/api-keys.
Your key will look like: bb_sk_xxxxxxxxxxxx
Choose your AI tool:
Add to your claude_desktop_config.json:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"beeboo": {
"command": "npx",
"args": ["-y", "@beeboo/mcp-server"],
"env": {
"BEEBOO_API_KEY": "bb_sk_your_key_here"
}
}
}
}Then restart Claude Desktop.
Add to your Cursor settings (~/.cursor/mcp.json or via Settings > MCP):
{
"mcpServers": {
"beeboo": {
"command": "npx",
"args": ["-y", "@beeboo/mcp-server"],
"env": {
"BEEBOO_API_KEY": "bb_sk_your_key_here"
}
}
}
}Add to your Windsurf MCP configuration:
{
"mcpServers": {
"beeboo": {
"command": "npx",
"args": ["-y", "@beeboo/mcp-server"],
"env": {
"BEEBOO_API_KEY": "bb_sk_your_key_here"
}
}
}
}npm install -g @beeboo/mcp-serverThen use beeboo-mcp-server instead of npx @beeboo/mcp-server.
Related MCP server: Finizi B4B MCP Server
Tool | Description |
| Search the knowledge base using semantic search |
| Add a new entry to the knowledge base |
| List all knowledge base entries |
| Request human approval for an action |
| Check status of an approval request |
| List all approval requests (with optional filter) |
| Create a work request for the team |
| List all work requests (with optional filter) |
Once configured, you can ask your AI assistant:
Knowledge Base:
"Search the knowledge base for deployment procedures"
"Add to the knowledge base: our AWS account ID is 123456789"
"List all knowledge entries"
Approvals:
"I need approval to delete the staging database"
"Check if approval abc123 has been approved"
"Show me all pending approvals"
Work Requests:
"Create a high-priority request to update the SSL certificate"
"List all open work requests"
Variable | Required | Default | Description |
| Yes | — | Your BeeBoo API key |
| No |
| API endpoint |
Test the server locally:
# List available tools
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | BEEBOO_API_KEY=your_key node index.js
# Test a tool call
echo '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"beeboo_knowledge_list","arguments":{}}}' | BEEBOO_API_KEY=your_key node index.jsMake sure you've set the BEEBOO_API_KEY in your MCP configuration.
Restart your AI tool (Claude Desktop, Cursor, etc.)
Check the configuration file path is correct
Verify the JSON syntax is valid
Check your API key is valid
Ensure you have network connectivity
Check the BeeBoo status at status.beeboo.ai
# Clone the repo
git clone https://github.com/beeboo-ai/beeboo.git
cd beeboo/mcp-server
# Install dependencies
npm install
# Run locally
BEEBOO_API_KEY=your_key npm startMIT
Check the status of an approval request
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The approval request ID to check |
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 only states the action ('Check the status') without disclosing behavioral traits like whether this is a read-only operation, what the response format includes (e.g., status values like 'pending', 'approved'), error handling, or rate limits. This leaves significant gaps for a tool with no annotation coverage.
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 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 the tool returns (e.g., status details, timestamps) or behavioral aspects like error cases. For a tool with no structured data to rely on, this minimal description leaves the agent under-informed.
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 'id' parameter documented as 'The approval request ID to check'. The description adds no additional meaning beyond this, such as format examples or validation rules, 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 tool's purpose as 'Check the status of an approval request' with a specific verb ('Check') and resource ('approval request'). It distinguishes itself from siblings like 'beeboo_approval_request' (likely for creating requests) and 'beeboo_approvals_list' (likely for listing approvals), but doesn't explicitly differentiate them.
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. It doesn't mention prerequisites (e.g., needing an existing approval request ID), exclusions, or comparisons to siblings like 'beeboo_approvals_list' for broader status checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Request human approval for an action. Use this when you need explicit permission before proceeding with a potentially impactful operation.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Brief description of what needs approval | |
| description | Yes | Detailed explanation of the request and why approval is needed |
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 discloses that this tool is for requesting approval (implying a human-in-the-loop workflow) and mentions 'potentially impactful operation,' hinting at risk or sensitivity. However, it lacks details on response format, timeout behavior, error conditions, or how approvals are processed, leaving gaps in behavioral understanding.
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 two sentences, front-loaded with the core purpose and followed by usage guidance. Every word earns its place, with no redundancy or fluff, making it highly efficient and easy to parse.
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 (a human-approval workflow with 2 parameters), no annotations, and no output schema, the description is reasonably complete. It covers purpose and usage well but lacks details on behavioral outcomes (e.g., what happens after submission). For a tool without annotations or output schema, it could benefit from more context on the approval process.
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 both parameters ('title' and 'description') well-documented in the schema. The description does not add any additional meaning or examples beyond what the schema provides (e.g., it doesn't clarify format or best practices for the parameters). Baseline 3 is appropriate as the schema handles 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 specific action ('Request human approval') and the resource ('for an action'), with a precise verb+resource combination. It distinguishes this tool from its siblings like 'beeboo_approval_check' (which likely checks status) and 'beeboo_approvals_list' (which lists approvals), making the purpose unambiguous.
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 explicitly states when to use this tool: 'Use this when you need explicit permission before proceeding with a potentially impactful operation.' This provides clear context for invocation and distinguishes it from alternatives like 'beeboo_request_create' (which might be for general requests) or 'beeboo_approval_check' (for checking status).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all approval requests with optional status filter
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | Filter by status: pending, approved, or rejected |
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. While 'List' implies a read-only operation, it doesn't specify whether this requires authentication, what format the results come in (e.g., paginated list), or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 immediately communicates the core functionality. Every word earns its place - 'List all approval requests' establishes the purpose, and 'with optional status filter' adds the key parameter context 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?
For a simple list tool with one optional parameter and no output schema, the description is adequate but has gaps. It doesn't explain what information is returned about each approval request, whether results are paginated, or how this differs from similar sibling tools. Given the lack of annotations and output schema, more context about the return format would be 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?
The description mentions the 'optional status filter' which aligns with the single parameter in the schema. Since schema description coverage is 100% (the parameter already has a clear description with enum values), the description adds minimal value beyond what's already documented in the structured schema. This meets the baseline expectation when schema coverage is high.
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 ('List') and resource ('all approval requests') with a specific scope ('with optional status filter'), making the purpose immediately understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'beeboo_requests_list' or 'beeboo_approval_check', which could cause confusion about when to use each.
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. With sibling tools like 'beeboo_requests_list' and 'beeboo_approval_check' available, there's no indication of whether this tool is for approval-specific requests versus general requests, or whether it should be used instead of checking individual approvals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add a new entry to the BeeBoo knowledge base
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Title of the knowledge entry | |
| content | Yes | Content/body of the entry | |
| tags | No | Optional tags for categorization |
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 'Add a new entry' which implies a write operation, but doesn't cover critical aspects like permissions required, whether the addition is immediate or pending approval, potential rate limits, or what happens on success/failure. This leaves significant gaps for a mutation 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 directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to 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 complexity of a write operation with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns, error conditions, or behavioral nuances like approval workflows suggested by sibling tools. For a mutation tool, more context is needed to ensure safe and correct usage.
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 clear documentation for title, content, and tags parameters. The description adds no additional semantic information beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as 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 'Add' and the resource 'new entry to the BeeBoo knowledge base', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like beeboo_knowledge_list or beeboo_knowledge_search, which handle reading rather than adding entries.
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. It doesn't mention prerequisites, such as whether approval is needed (hinted by sibling tools like beeboo_approval_request), or clarify its role relative to other knowledge base tools like beeboo_knowledge_search for finding entries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all knowledge base entries
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 'List all knowledge base entries', implying a read-only operation that retrieves all entries without filtering. However, it doesn't describe potential behaviors like pagination, rate limits, authentication requirements, or what 'all' entails (e.g., scope or limits), leaving significant gaps.
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 front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.
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 (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavioral traits, output format, or sibling differentiation. For a list operation, it should ideally mention scope or limitations, but it meets the basic requirement for a simple tool.
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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied since there are no parameters to document, and the description doesn't introduce confusion.
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 'List all knowledge base entries' clearly states the verb ('List') and resource ('knowledge base entries'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'beeboo_knowledge_search', which might also retrieve knowledge entries but with filtering capabilities.
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 'beeboo_knowledge_search' or 'beeboo_knowledge_add'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the BeeBoo knowledge base for information using semantic search
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query - can be natural language |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Search' implies a read operation, it doesn't specify important behavioral aspects like whether this is a real-time search, if there are rate limits, authentication requirements, result format, or pagination behavior. The mention of 'semantic search' is helpful but insufficient.
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 communicates the essential information without any wasted words. It's appropriately sized for a single-parameter search tool and front-loads the core functionality.
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 search tool with no annotations and no output schema, the description is inadequate. It doesn't explain what kind of results to expect, how results are ranked, whether there are limitations on query complexity, or how to interpret search results. The mention of 'semantic search' is the only contextual element provided.
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 single parameter 'query' well-documented as accepting natural language. The description adds minimal value beyond the schema by mentioning 'semantic search' which contextualizes the query parameter, but doesn't provide additional syntax or format details.
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 ('Search') and target resource ('BeeBoo knowledge base for information'), with the method 'using semantic search' providing specific implementation detail. However, it doesn't explicitly differentiate from sibling tools like 'beeboo_knowledge_list' which might also retrieve knowledge base content.
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 'beeboo_knowledge_list' or 'beeboo_knowledge_add'. There's no mention of appropriate contexts, prerequisites, or exclusions for this search functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a work request for the team. Use this to queue up tasks that need human attention or execution.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Brief title of the work request | |
| description | No | Detailed description of what needs to be done | |
| priority | No | Priority level: low, medium, high, or critical |
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 'queue up tasks' implying a write operation, but fails to detail critical aspects like required permissions, whether the creation is immediate or asynchronous, error handling, or what happens after submission (e.g., notification triggers). This leaves significant gaps for a mutation 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 highly concise and front-loaded, consisting of two sentences that directly state the tool's purpose and usage without any wasted words. Each sentence earns its place by providing essential information efficiently, making it easy for an agent to parse 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 complexity as a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., side effects, response format), doesn't explain return values, and provides minimal context beyond basic usage, making it inadequate for fully informed tool selection and 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?
The input schema has 100% description coverage, clearly documenting all three parameters (title, description, priority) with details like enum values for priority. The description adds no additional parameter semantics beyond what the schema provides, such as formatting examples or constraints, so it meets the baseline for high schema coverage without extra value.
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 ('Create a work request') and resource ('for the team'), providing a specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'beeboo_approval_request' or 'beeboo_requests_list', which might handle related request operations, leaving room for ambiguity about when to use this specific creation tool.
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 implied usage guidance by stating 'Use this to queue up tasks that need human attention or execution,' which suggests context for when to apply it. However, it lacks explicit when-not-to-use scenarios or direct alternatives, such as how it differs from 'beeboo_approval_request' for approvals or 'beeboo_requests_list' for viewing requests, leaving the agent to infer distinctions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all work requests with optional status filter
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | Filter by status: open, in_progress, or resolved |
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 listing with optional filtering but doesn't cover critical aspects like pagination, rate limits, authentication needs, return format, or whether it's read-only. For a list tool with zero annotation coverage, this leaves significant gaps in understanding its 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 front-loads the core action ('List all work requests') and adds the optional filter detail. There is no wasted wording, and it's appropriately sized for the tool's simplicity.
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 'work requests' entail, the scope of 'all', how results are returned, or any behavioral constraints. For a list tool with no structured support, more context is needed to fully understand its operation.
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%, with the parameter 'status' fully documented in the schema (including enum values and description). The description adds minimal value by mentioning the optional status filter but doesn't provide additional semantics beyond what the schema already covers. This meets the baseline for high schema coverage.
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 ('List all work requests') and resource ('work requests'), making the purpose immediately understandable. It distinguishes from siblings like 'beeboo_request_create' (creation vs listing) and 'beeboo_approvals_list' (requests vs approvals), though it doesn't explicitly differentiate them. The optional status filter is mentioned, adding specificity.
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. It doesn't mention prerequisites, context for filtering, or comparisons with siblings like 'beeboo_approvals_list' or 'beeboo_knowledge_search'. The description only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every tool has a clearly distinct purpose with no ambiguity. The tools are cleanly separated into three functional groups: approvals (check, request, list), knowledge base (add, list, search), and work requests (create, list). There is no overlap in functionality between these groups or within each group.
All tools follow a perfect and consistent naming pattern: 'beeboo_' prefix followed by resource name (approval/knowledge/request) and action verb (check/request/list/add/search/create). The pattern is uniformly applied across all 8 tools with no deviations in style or structure.
With 8 tools, the server is well-scoped for its purpose of handling approvals, knowledge management, and work requests. Each tool earns its place by providing distinct functionality within these three domains, neither feeling too sparse nor overloaded.
The tool surface provides excellent coverage for the three domains, with CRUD-like operations for each: approvals (request, check, list), knowledge (add, list, search), and work requests (create, list). Minor gaps include no update/delete operations for knowledge entries or work requests, but agents can work around this limitation.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Give your AI agents the tools to build, manage, and run automation workflows.
Create Tasks and run Workflows in Bleep from Claude, ChatGPT, and other AI assistants.
Runtime permission, approval, and audit layer for AI agent tool execution.
One place to build, share, and govern the skills and tools your AI agents use at work.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/TGM-Ventures/beeboo-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
