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Local-first MCP server that indexes a codebase into SQLite once and then lets AI agents search it instantly — offline, private, and with zero dependencies via literal substring search, incremental indexing, per-language stats, and root listing.

Local-first MCP server that indexes a codebase into SQLite once and then lets AI agents search it instantly — offline, private, and with zero dependencies via literal substring search, incremental indexing, per-language stats, and root listing.

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Python
Local

search

Find exact lines in a codebase by looking up a literal substring in indexed files. Returns matching lines with file path and line number for offline, private code lookup.

Instructions

Search previously indexed files for a literal substring. Returns matching lines with file path and line number.

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"
  }
}

Input Schema

NameRequiredDescriptionDefault
rootNoOptional project root to search within
queryYesLiteral substring to search for
max_resultsNoMaximum results (default 100)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that matching is literal (not regex/fuzzy) and describes the return format, providing useful behavioral detail. It does not discuss edge cases like case sensitivity, but those are not critical for a search tool.

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?

The description is two sentences with no fluff, front-loading the core purpose and then adding return-value detail. Every word earns its place.

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?

For a low-complexity tool with a fully descriptive schema, the description covers the essential behavioral and output aspects. It does not need to explain return values (no output schema exists, but the description already does) or additional edge cases.

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?

Schema coverage is 100%, with all parameters already described clearly. The description reinforces the 'literal substring' aspect for the query parameter but does not add significant meaning beyond what the schema provides.

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 previously indexed files for a literal substring and specifies the output (matching lines with file path and line number). This distinctively differentiates it from siblings like index, stats, and roots.

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 phrase 'previously indexed files' implies the tool should be used after indexing has occurred, giving clear context. It does not explicitly name alternatives or exclusions, but the context is unambiguous enough for a simple search tool.

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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