A
licenseA
qualityF
maintenanceA Model Context Protocol server that enables AI assistants to interact with Confluence content, supporting operations like retrieving, searching, creating, and updating pages and spaces.
9
19
12
MIT
Glean MCP Server
by gleanwork
The Glean MCP Server enables seamless integration with Glean's enterprise knowledge through three main functions:
Company Search: Query Glean's content index to find relevant company documents with advanced filtering options by data sources.
Chat: Interact with Glean's AI assistant for conversational support, including contextual messaging, message history, and citations.
People Profile Search: Search the company's directory to find employee information with filters like department, location, or role.
The server integrates with MCP-enabled clients such as Claude Desktop, Windsurf, or Cursor for compliance.
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., "@Glean MCP Serversearch for Q4 sales reports"
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.
Glean Local MCP Server Monorepo
IMPORTANT
This repository is archived / no longer maintained.
Do not use this repository for new MCP setups. It predates Glean’s managed remote MCP server and is no longer the supported path for connecting MCP-compatible hosts to Glean.
What to use instead
Use the managed Glean MCP server built into your Glean instance:
Using the Glean MCP Server — end-user setup for supported MCP hosts.
Set up Glean MCP server — admin setup and enablement.
About Glean MCP server — overview of Glean’s managed MCP server.
For local command-line workflows, use Glean CLI instead of this local MCP server.
Related MCP server: MCP Boilerplate
Repository status
This repository is retained for historical reference only. Issues and pull requests have been closed as part of archival cleanup, and no new feature work or bug fixes are planned here.
The local packages in this repository should not be used for new installations. Published user-facing local MCP packages are being retired in favor of the managed Glean MCP server.
What this repository used to contain
This monorepo previously contained packages for running and supporting a local stdio-based MCP server for Glean:
@gleanwork/local-mcp-server— a local MCP server that exposed Glean Search, Chat, People Search, and document-reading tools.@gleanwork/mcp-server-utils— shared utilities used by the local MCP server packages.
These packages predate the managed remote MCP server and are no longer the recommended or supported MCP integration path.
Historical reference
Existing install, Docker, and client-configuration instructions have intentionally been removed from the main documentation so new users are not directed toward an unsupported setup. If you need to understand the old implementation, inspect the repository history.
License
MIT License — see the LICENSE file for details.
Available Tools
3 toolschatC
Chat with Glean Assistant using Glean's RAG
Example request:
{
"message": "What are the company holidays this year?",
"context": [
"Hello, I need some information about time off.",
"I'm planning my vacation for next year."
]
}
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Optional previous messages for context. Will be included in order before the current message. | |
| message | Yes | The user question or message to send to Glean Assistant. |
TDQS
C2.9/5.0
Behavior2/5
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 only states the basic action ('Chat') and includes an example request, but doesn't cover critical aspects like authentication needs, rate limits, response format, or any side effects. This is inadequate for a tool with potential complexity in AI interactions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. The example request is relevant but could be more integrated; overall, it's efficient with minimal waste, though the formatting as a code block might slightly affect readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an AI chat tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, how errors are handled, or any behavioral traits, leaving significant gaps for the agent to understand the tool's full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters ('message' and 'context'). The description adds minimal value beyond the schema by showing an example request, but doesn't provide additional semantic context or usage nuances. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Chat with Glean Assistant using Glean's RAG.' It specifies the verb ('Chat') and resource ('Glean Assistant'), but doesn't explicitly differentiate it from sibling tools like 'company_search' or 'people_profile_search', which are search-focused rather than conversational AI interactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 lacks any mention of sibling tools or scenarios where this chat tool is preferred over search tools, leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
company_searchC
Find relevant company documents and data
Example request:
{
"query": "What are the company holidays this year?",
"datasources": ["drive", "confluence"]
}
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| datasources | No | Optional list of data sources to search in. Examples: "github", "gdrive", "confluence", "jira". | |
| query | Yes | The search query. This is what you want to search for. |
TDQS
C2.6/5.0
Behavior2/5
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 'Find[s] relevant company documents and data', which implies a read-only search operation, but doesn't cover critical aspects like permissions, rate limits, response format, or error handling. The example adds some context but is insufficient for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the purpose statement, followed by a helpful example. However, the example is formatted as a code block, which adds visual clutter but doesn't detract significantly from clarity. It's efficient with no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 search function with 2 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage guidelines, leaving gaps that could hinder an AI agent's ability to invoke it correctly. The example provides some context but doesn't compensate for the missing structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents both parameters ('query' and 'datasources') with clear descriptions. The description adds minimal value beyond the schema through the example, which shows usage but doesn't explain parameter semantics further. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'Find relevant company documents and data', which is clear but somewhat vague. It specifies the resource ('company documents and data') but lacks a precise verb beyond 'Find' and doesn't differentiate from sibling tools like 'chat' or 'people_profile_search'. The example helps but doesn't fully clarify the scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'chat' or 'people_profile_search'. It includes an example that implies usage for querying company information, but there are no explicit instructions on context, prerequisites, or exclusions. This leaves the agent without clear usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
people_profile_searchC
Search for people profiles in the company
Example request:
{
"query": "Find people named John Doe",
"filters": {
"department": "Engineering",
"city": "San Francisco"
},
"pageSize": 10
}ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filters | No | Allowed facet fields: email, first_name, last_name, manager_email, department, title, location, city, country, state, region, business_unit, team, team_id, nickname, preferred_name, roletype, reportsto, startafter, startbefore, industry, has, from. Provide as { "facet": "value" }. | |
| pageSize | No | Hint to the server for how many people to return (1-100, default 10). | |
| query | No | Free-text query to search people by name, title, etc. |
TDQS
C2.9/5.0
Behavior2/5
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 mentions searching but doesn't describe key traits like whether this is a read-only operation, pagination behavior beyond 'pageSize', rate limits, authentication needs, or what happens with large result sets. The example shows a request format but lacks 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose in the first sentence. The example request is relevant but could be more concise. Overall, it avoids unnecessary verbosity, though the example takes up space without adding significant guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with three parameters, no annotations, and no output schema, the description is incomplete. It lacks information on return values, error handling, and behavioral traits like pagination or rate limits. The example helps but doesn't compensate for the missing contextual details needed for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds an example that illustrates usage but doesn't provide additional semantic meaning beyond what's in the schema descriptions. The example clarifies the structure of 'filters' as an object, but this is implied by the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for people profiles in the company.' It specifies the verb ('Search') and resource ('people profiles'), and distinguishes it from sibling tools like 'chat' and 'company_search' by focusing on people profiles rather than chat or company data. However, it doesn't explicitly differentiate from potential similar search tools beyond the resource scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like 'company_search' or explain scenarios where one might be preferred over the other. The example request is helpful for syntax but doesn't offer usage context or exclusions.
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.
3 tool updates
v1.0.0- First observed
chat - First observed
company_search - First observed
people_profile_search
TDQS
B3/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: 'chat' is for conversational interaction with an assistant, 'company_search' is for finding documents and data, and 'people_profile_search' is for locating employee profiles. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the task.
Naming Consistency4/5
The tool names follow a consistent snake_case pattern and are descriptive, but there is a minor deviation: 'chat' uses a simple verb, while the other two tools use a noun_noun structure (e.g., 'company_search'). This slight inconsistency does not hinder readability or predictability significantly.
Tool Count3/5
With only 3 tools, the set feels thin for a server named 'Glean MCP Server', which implies broader capabilities in information retrieval and assistance. While the tools cover chat, document search, and people search, the scope might benefit from additional tools for more granular operations or updates, making the count borderline appropriate.
Completeness3/5
The tools provide basic search and chat functionalities, but there are notable gaps. For example, there is no tool for updating or managing data (e.g., creating documents or modifying profiles), and the chat tool lacks explicit support for follow-up actions or context persistence. This limits the server's ability to handle full lifecycle operations in its domain.
Maintenance
ActivityStale
ResponsivenessSyncing
Resources
Unclaimed servers have limited discoverability.
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