lnkz-mcp

Search connected context

search_context

Read-only

Search across LNKZ conversations and all connected sources—Slack, Jira, Figma, docs, and federated MCP servers—in one query, with per-source failure reporting for transparency.

Instructions

Searches LNKZ conversations plus every configured connector (Slack, Jira, Figma, documentation feeds, and any federated MCP server) in one call, reporting per-source failures instead of hiding them.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_context"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes
`sources`	No
`excludeSources`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Sep 7, 2026

TDQS

A3.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true and openWorldHint=true, so the description doesn't need to cover safety. It adds a valuable behavioral detail: 'reporting per-source failures instead of hiding them,' which is not in the annotations and helps the agent set expectations for error handling. No contradiction with annotations.

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 a single, front-loaded sentence that states the scope and a key behavior without fluff. It is concise and easy to parse. However, it omits any parameter details, which would improve completeness, but the structure itself is efficient.

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 has 4 parameters (with one required), no output schema, and no parameter details in the description, the definition is incomplete. An agent cannot deduce how to specify sources or exclude sources, what the limit does, or how to handle the query. The description should at least mention the query parameter and suggest the sources/excludeSources options to be minimally usable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% — the description does not mention any of the four parameters (query, limit, sources, excludeSources). With no schema explanation, the description must compensate, but it provides no guidance on how to use these parameters, leaving the agent without necessary context for correct invocation.

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 searches LNKZ conversations plus all configured connectors in a single call, and explicitly mentions the unique behavior of reporting per-source failures. This distinguishes it from siblings like search_conversations, which likely only search LNKZ conversations.

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 implies usage: use this when you need a multi-source search across all connectors. It clearly lists the connectors included (Slack, Jira, Figma, etc.), which helps an agent understand scope. However, it does not explicitly state when NOT to use this or mention alternatives like search_conversations for single-source queries, so it lacks direct exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.