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Platano78

Smart-AI-Bridge

by Platano78

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Search codebases with natural-language questions to locate where specific features are implemented, returning a summary and matched file:line references instead of raw contents.

Instructions

Natural-language search across the codebase: combines grep-style matching with optional LLM summarization to answer 'where is X handled?' or 'what files implement Y?' Returns a summary + the matching file:line list, not raw file contents. Use when you DON'T already know which file to look at. For a deep analysis of ONE known file, use analyze_file. For a structured question across a known set of files (glob patterns), use batch_analyze. depth:'shallow' is fast grep; depth:'deep' adds LLM-generated context per match. Read-only: walks the filesystem and reads matched files but never writes. Returns: {success, summary (LLM- or template-generated answer), files_found:[paths], search_patterns:[strings actually grepped], evidence:[...] (capped at 15), tokens_saved, processing_time_ms, depth, backend_used}. The evidence entry shape depends on depth: shallow returns {file, line, snippet} per matching line; deep returns {file, matches:[{line, context}]}, grouping each file's matches with surrounding context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
optionsNo
questionYesNatural language question about the codebase (e.g., "where is user authentication handled?")

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv2.14.0
    • changedInput schema / properties / options / properties / backend / enum
      Previous value: -[
      -  "auto",
      -  "groq",
      -  "glm",
      -  "qwen3",
      -  "deepseek"
      -]New value: +[
      +  "auto",
      +  "groq",
      +  "glm",
      +  "deepseek"
      +]
  2. Changed1 schema field changed
    • changedInput schema / properties / options / properties / backend / enum
      Previous value: -[
      -  "auto",
      -  "groq",
      -  "qwen3",
      -  "deepseek"
      -]New value: +[
      +  "auto",
      +  "groq",
      +  "glm",
      +  "qwen3",
      +  "deepseek"
      +]
  3. Addedv1.3.2

TDQS

A4.8/5.0
Behavior5/5

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 the tool is read-only ('never writes'), returns a summary + file:line list instead of raw contents, explains the shallow vs deep depth difference, caps evidence at 15, and lists the full return object. This is thorough and accurate.

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 well-structured paragraph, but it is longer than strictly necessary. However, every sentence contributes value—purpose, usage, behavior, and return format—so it is efficient given the tool's complexity.

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?

The tool is complex (nested options, multiple backends, depth-dependent output), and the description covers all necessary aspects: what it does, when to use it, behavioral guarantees, parameter nuances, and the exact return structure including unusual fields like tokens_saved and processing_time_ms. It even notes the evidence cap.

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?

The schema descriptions cover question and the sub-fields of options (depth, scope, backend, maxFiles), but only at a surface level. The description adds crucial semantics for depth ('fast grep' vs 'adds LLM-generated context per match') and describes the evidence shape per depth. Since schema coverage is 50%, it compensates well, though scope/backend are left to the schema.

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 states a specific verb ('search') and resource ('codebase'), and clarifies what it answers ('where is X handled?' / 'what files implement Y?'). It also explicitly contrasts with siblings (analyze_file, batch_analyze) by naming their use cases, making the purpose unambiguous.

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

Usage Guidelines5/5

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

Provides explicit when-to-use ('when you DON'T already know which file to look at') and when-not-to-use, naming alternatives for each: 'analyze_file' for a single known file, 'batch_analyze' for structured questions over known globs. This leaves no ambiguity about tool selection.

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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