Your company's brain for AI agents. Cited, permission-aware knowledge across every system.
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.
Average 4.7/5 across 3 of 3 tools scored.
Each tool serves a distinct purpose: get_source_detail provides provenance for previous queries, list_domains lists available knowledge domains, and query_knowledge performs the actual search. No overlap in functionality.
All tool names follow a consistent verb_noun pattern with lowercase and underscores: get_source_detail, list_domains, query_knowledge. The naming is uniform and predictable.
With only three tools, the set is minimal but well-scoped for a read-only knowledge query interface. It covers the essential workflow without unnecessary tools, though a few more (e.g., to list query history) could be beneficial.
The tools cover the core workflow: domain exploration (list_domains), knowledge querying (query_knowledge), and provenance retrieval (get_source_detail). Missing is a way to list past queries or manage the knowledge base, but these are outside the apparent intended scope.
get_source_detailAReturn per-chunk source provenance for a previous query — document path, lifecycle state, embedding timestamp, contributor, last-updated — useful for verifying a citation or surfacing trust signals to a downstream system. Pass a query_id returned by an earlier query_knowledge call. Returns 404 if the query_id is unknown OR belongs to a different tenant (indistinguishable to prevent info-leak). Zero Knowledge Tokens consumed.
| Name | Required | Description | Default |
|---|---|---|---|
| query_id | Yes | UUID returned in the structured-metadata block of a prior `query_knowledge` response. Tenant-scoped — cross-tenant 404. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint, destructiveHint=false), the description adds that zero Knowledge Tokens are consumed and that 404 is returned for unknown or cross-tenant query IDs (indistinguishable to prevent info-leak). This is valuable behavioral 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 two sentences: first explains purpose and output, second explains input and error behavior. No redundant information; every sentence is purposeful.
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 a single parameter and no output schema, the description covers input origin, output fields, error handling, and token consumption. It is complete for the tool's complexity.
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%, but the description adds meaningful context: query_id is a UUID from the structured-metadata block of a prior query_knowledge response, and that it is tenant-scoped. This augments the schema's description.
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 explicitly states it returns per-chunk source provenance for a previous query, listing specific fields (document path, lifecycle state, etc.). It clearly distinguishes from sibling tools (list_domains, query_knowledge) by focusing on provenance retrieval.
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 specifies to pass a query_id from a prior query_knowledge call, which is clear and actionable. It does not explicitly state when to avoid this tool, but the context of 'verifying a citation or surfacing trust signals' implies its use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_domainsAList the taxonomy domains the company has indexed — with document counts, expert counts, and coverage levels — so an agent can decide whether to query before spending a Knowledge Token. Returns one row per domain with the canonical taxonomy_domain slug, document/chunk counts, expert count, coverage level (expert | partial | none), the single_expert risk flag, and the top contributor by authority. Use the slug as the domain filter on a follow-up query_knowledge call. Zero Knowledge Tokens consumed.
| Name | Required | Description | Default |
|---|---|---|---|
| coverage_filter | No | Optional comma-separated subset of expert,partial,none. Default: all three. Unknown tokens 400. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, destructiveHint, idempotentHint), the description adds that zero Knowledge Tokens are consumed, details the returned fields, and explains parameter behavior (default, error for unknown tokens), providing comprehensive behavioral 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 concise (4 sentences), front-loaded with purpose, and each sentence adds value: purpose, return details, follow-up usage, and cost.
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 (one optional parameter, no output schema), the description fully covers what is returned, parameter behavior, cost, and integration with sibling tool, leaving 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 coverage is 100%, and the description only repeats the schema's parameter description without adding new semantic information about the input parameter.
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 lists taxonomy domains with document counts, expert counts, and coverage levels, and explicitly differentiates from siblings by explaining it helps decide whether to query before spending a Knowledge Token.
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 states when to use (before querying to avoid spending tokens) and instructs to use the returned slug as the domain filter on a follow-up query_knowledge call, providing clear usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_knowledgeASearch the company's connected knowledge across every source — Drive, SharePoint, Confluence, Slack, Notion — with cited synthesized answers, lifecycle awareness, and refusal-on-weak-context. Returns a written answer with [n] citations plus the ranked source chunks. Modes: fast (1,500 kT — retrieval-only, no synthesis), standard (12,500 kT — default; synthesized answer over the top retrieval set), deep (25,000 kT — wider retrieval + premium synthesis for complex questions). Pick the cheapest tier that answers the question. Responses are capped at 25,000 output tokens per Claude Connectors policy; if truncated, structured metadata carries truncated: true and query_id so the agent can call get_source_detail for full provenance.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | fast | standard | deep. Defaults to `standard`. `fast` returns chunks only (no synthesized answer); `standard` and `deep` return a synthesized answer with [n] citations + the source chunks. | |
| query | Yes | Natural-language query (1–2000 characters). Be specific — results are ranked by authority + relevance, not keyword overlap. | |
| domain | No | Optional taxonomy domain filter (e.g. 'engineering.platform'). Use `list_domains` to discover valid values for the tenant. | |
| max_sources | No | Number of source chunks to return (1–20, default 5). The 25K token cap may force fewer results regardless of this value. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond annotations by detailing mode behaviors, token limits, truncation handling, refusal-on-weak-context, and lifecycle awareness. It aligns with readOnlyHint and idempotentHint, adding rich behavioral context without contradiction.
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 and front-loaded with the core function. It is slightly lengthy but each sentence adds value—covering modes, tokens, truncation, and references to siblings. Could be trimmed slightly, but highly informative.
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 params, no output schema), the description thoroughly explains return format (synthesized answer with citations and source chunks), truncation metadata, mode selection, and parameter behavior. It also references siblings for additional context.
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, but the description adds practical meaning by explaining mode differences (e.g., fast is retrieval-only) and mentioning the token cap's impact on max_sources. This enhances param understanding.
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: searching company knowledge across multiple sources and returning a synthesized answer with citations. It distinguishes itself from siblings by mentioning that truncated results can be followed up with get_source_detail and that domain filtering is available via list_domains.
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 advises selecting the cheapest mode ('fast', 'standard', 'deep') based on question complexity, and notes that get_source_detail can provide full provenance for truncated results. It implies context for when to use this tool over siblings but does not explicitly state exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
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
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Sign in to create a connector for this server.