mcp-server
Server Details
Your company's brain for AI agents. Cited, permission-aware knowledge across every system.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- Quelvio/quelvio-mcp-server
- GitHub Stars
- 0
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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 clear, non-overlapping purpose: list_domains for exploring available domains, query_knowledge for searching and synthesizing answers, and get_source_detail for verifying source provenance. An agent can easily determine which tool to use at each step.
All tool names follow a consistent verb_noun pattern in snake_case (list_domains, query_knowledge, get_source_detail). This makes it predictable for an agent to understand the action and target of each tool.
With only 3 tools, the server is well-scoped for its purpose of knowledge retrieval. Each tool plays an essential role in the workflow (explore, query, verify), and no redundant tools exist.
The tool set covers the entire user journey: listing domains to inform queries, performing the query with multiple modes, and retrieving detailed source provenance. There are no obvious gaps for the stated purpose.
Available Tools
3 toolsget_source_detailARead-onlyIdempotentInspect
Return 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses 404 indistinguishability for info-leak prevention and zero token consumption, adding value beyond annotations which already indicate read-only, idempotent, non-destructive 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?
Three efficient sentences: purpose with fields, usage instruction, and error/consumption note. No wasted words, front-loaded with key action.
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 read-only tool with one parameter and good annotations, description covers purpose, usage, error behavior, and token cost. No missing critical info.
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?
Only one parameter with 100% schema coverage; description repeats schema info (UUID from prior query, tenant-scoped). Baseline 3 is appropriate as description adds minimal extra meaning.
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?
Description clearly states the tool returns per-chunk source provenance for a previous query, listing specific fields. This distinguishes it from siblings like list_domains and query_knowledge.
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 to pass a query_id from a prior query_knowledge call. Also explains 404 behavior for unknown or cross-tenant IDs, but does not explicitly say when not to use it or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_domainsARead-onlyIdempotentInspect
List 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds value by specifying zero Knowledge Tokens consumed and detailing return fields including coverage level and single_expert risk flag.
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?
Single, well-structured paragraph that front-loads the main purpose, then provides details on return fields and usage. Every sentence contributes useful 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 (one optional parameter, no output schema, rich annotations), the description is complete: it explains return fields, cost, and how to chain with query_knowledge.
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%, with parameter description already defining acceptable values and behavior. The tool description does not add further parameter-specific meaning beyond reinforcing coverage levels.
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 it lists taxonomy domains with document counts, expert counts, and coverage levels. It distinguishes from siblings by framing it as a prerequisite to query_knowledge.
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 the tool helps decide whether to query before spending a Knowledge Token and that it consumes zero tokens. Provides guidance on using the slug in a follow-up query_knowledge call. Could mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_knowledgeARead-onlyIdempotentInspect
Search 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive. Description adds value by detailing return format (synthesized answer + citations), refusal-on-weak-context, and truncation behavior with query_id for follow-up.
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?
Single paragraph that is information-dense and well-structured: starts with purpose, then modes, then cap and follow-up. No wasted sentences.
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?
Covers return format, mode options, limitations, and follow-up. Also mentions lifecycle awareness and refusal-on-weak-context. No output schema but description compensates. Sibling tools referenced appropriately.
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 covers all parameters (100% coverage). Description adds context on tier selection cost, ranking criteria, and token cap interaction, complementing the schema effectively.
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 'Search' and the resource 'company's connected knowledge', and distinguishes from siblings by referencing `get_source_detail` for provenance and `list_domains` for domain filtering.
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?
Provides explicit guidance on mode selection ('Pick the cheapest tier'), token caps, and follow-up for truncated responses. Does not explicitly state when not to use, but gives sufficient context.
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.
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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.
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