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no13productions

AI Agent History RAG MCP Server

search_conversations

Search conversation history using natural language queries to retrieve previous discussions, decisions, and compacted context. Supports filtering by project and date range, with hybrid search and analysis for relevance.

Instructions

Search conversation history for relevant context.

Use this to find:
- Previous discussions about a topic
- Decisions made in earlier sessions
- Context that was compacted away

Args:
    query: Natural language query
    project_filter: Limit to specific project path
    date_from: Inclusive lower timestamp bound. Accepts ISO-8601 datetime
        or date-only values such as 2026-06-13.
    date_to: Inclusive upper timestamp bound. Accepts ISO-8601 datetime
        or date-only values such as 2026-06-15.
    limit: Maximum results (default 5, min 1, max 50)
    use_hybrid: Use hybrid search (vector + BM25) for better results
        (default True)
    enable_analysis: Enable query analysis and result evaluation for
        improved relevance (default True). Adds 'analysis' and 'evaluation'
        to response.
    enable_synthesis: Enable result synthesis to combine multiple results
        into a coherent summary (default False). Adds 'synthesis' to response
        with key_points and deduplicated content.
    include_debug: Include detailed timing metrics and decision tracking
        in response (default False). Useful for debugging and performance
        analysis. Adds 'metrics' to response.

Returns:
    Dict with results list and metadata. When enable_analysis=True, includes:
    - analysis: Query intent, detected technologies, key terms
    - evaluation: Relevance score, completeness assessment
    When enable_synthesis=True, includes:
    - synthesis: Primary content, key points, code snippets
    When include_debug=True, includes:
    - metrics: Timing data (query_analysis_ms, search_ms, etc.), decisions made

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
date_toNo
date_fromNo
use_hybridNo
include_debugNo
project_filterNo
enable_analysisNo
enable_synthesisNo
Behavior4/5

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

Describes return value structure based on flags (analysis, synthesis, debug) and mentions default behaviors. With no annotations, this adequately discloses read-only search behavior and result shape.

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?

Well-structured with main purpose, Args, and Returns sections. Slightly long but each line adds value; front-loaded purpose sentence is effective.

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

Completeness4/5

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

Covers search functionality, parameter details, and return variations comprehensively. Lacks error handling or rate limits, but adequate given no output schema and 9 parameters.

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

Parameters5/5

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

All 9 parameters are thoroughly described in the Args section, including types, defaults, and effects (e.g., date_from: ISO-8601, enable_analysis: adds 'analysis' and 'evaluation'). Schema coverage is 0%, so description fully compensates.

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?

Clearly states 'Search conversation history for relevant context' and lists specific use cases (previous discussions, decisions, compacted context), effectively distinguishing from sibling tools like search_file_changes.

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?

Provides explicit 'Use this to find:' list of scenarios, guiding when to invoke. Does not explicitly mention when not to use or alternatives, but the context is sufficiently clear.

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

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