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Search LNKZ conversations

search_conversations
Read-only

Need to find a specific conversation? Search saved chats by title, summary, participant, tag, or message content to get ranked results.

Instructions

Full-text ranked search across saved chats by title, summary, participant, tag, or message content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.7/5.0
Behavior3/5

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

The readOnlyHint annotation already declares this as a read-only operation, and the description's 'search' verb aligns with that. The description adds the behavioral trait of 'ranked' results, which implies ordering by relevance, but provides no detail on pagination, result shape, or whether it returns full conversation objects or summaries. With annotations covering the safety profile, the description adds only marginal 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence states the core purpose without any filler. It efficiently conveys the action, resource, and scope. No redundant information, and the structure allows an agent to grasp the tool's function immediately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple search tool with two parameters and no output schema, the description is adequate but leaves gaps. It does not describe the return format (e.g., whether results include conversation IDs, metadata, or full messages), which could be critical for downstream processing. It also doesn't mention ranking criteria beyond 'ranked'. While the absence of an output schema heightens the need for return-value disclosure, the description remains functional for basic invocation.

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?

With 0% schema description coverage, the description must compensate for parameter meaning. It does so for the 'query' parameter by specifying the fields it matches (title, summary, participant, tag, message content), which is useful. However, it provides no elaboration on 'limit' beyond what the schema already shows (default, min, max). The description adds some value but does not fully cover all parameter nuances.

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 a specific action (search) on a specific resource (saved chats/conversations) and enumerates the searchable fields (title, summary, participant, tag, message content). It differentiates itself from siblings like get_conversation (retrieval) and list_conversations (listing) by focusing on ranked full-text search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (when searching across conversations) but provides no explicit guidance on alternatives or exclusions. It doesn't mention that search_context might be more appropriate for context-specific searches, nor does it state that this is not for retrieving a single conversation. The usage context is clear but underdeveloped.

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