Skip to main content
Glama

mcp-server

by com.quelvio

query_knowledge

Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNofast | 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.
queryYesNatural-language query (1–2000 characters). Be specific — results are ranked by authority + relevance, not keyword overlap.
domainNoOptional taxonomy domain filter (e.g. 'engineering.platform'). Use `list_domains` to discover valid values for the tenant.
max_sourcesNoNumber of source chunks to return (1–20, default 5). The 25K token cap may force fewer results regardless of this value.

TDQS

A4.6/5.0
Behavior5/5

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.

Conciseness4/5

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.

Completeness5/5

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.

Parameters4/5

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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 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.

Usage Guidelines4/5

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.

All mcp-server toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.6/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count4/5

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.

Completeness4/5

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.

Resources