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SammySnake-d

Fast Context MCP

by SammySnake-d

fast_context_search

Search a codebase using natural language queries. Returns relevant file paths with line ranges and suggested grep keywords for follow-up searches.

Instructions

AI-driven semantic code search using Windsurf's Devstral model. Searches a codebase with natural language and returns relevant file paths with line ranges, plus suggested grep keywords for follow-up searches. Parameter tuning guide:

  • tree_depth: Controls how much directory structure the remote AI sees before searching. If you get a payload/size error, REDUCE this value. If search results are too shallow (missing files in deep subdirectories), INCREASE this value.

  • max_turns: Controls how many search-execute-feedback rounds the remote AI gets. If results are incomplete or the AI didn't find enough files, INCREASE this value. If you want a quick rough answer, use 1. Response includes a [config] line showing actual parameters used — use this to decide adjustments on retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query (e.g. "where is auth handled", "database connection pool")
project_pathNoAbsolute path to project root. Empty = current working directory.
tree_depthNoDirectory tree depth for the initial repo map sent to the remote AI. Default 3. Use 1-2 for huge monorepos (>5000 files) or if you get payload size errors. Use 4-6 for small projects (<200 files) where you want the AI to see deeper structure. Auto falls back to a lower depth if tree output exceeds 250KB.
max_turnsNoNumber of search rounds. Each round: remote AI generates search commands → local execution → results sent back. Default 3. Use 1 for quick simple lookups. Use 4-5 for complex queries requiring deep tracing across many files. More rounds = better results but slower and uses more API quota.
max_resultsNoMaximum number of files to return. Default 10. Use a smaller value (3-5) for focused queries. Use a larger value (15-30) for broad exploration queries.
exclude_pathsNoDirectory/file patterns to exclude from tree and search context. Useful for reducing payload size on large repos. Examples: ['node_modules', 'dist', '.git', 'build', 'coverage', '*.min.*']
Behavior5/5

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

With no annotations provided, the description fully explains the search process: rounds of search-execute-feedback, auto fallback for tree depth if payload exceeds 250KB, and mentions API quota usage for more rounds. This provides comprehensive behavioral insight.

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 well-structured with a main overview and a parameter tuning guide. It is concise overall but the tuning guide could be slightly more compact. Still, it is clear and front-loaded with key purpose.

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?

Given 6 parameters, 100% schema coverage, no output schema, and only one sibling, the description covers purpose, behavior, tuning, and response content (including [config] line). It provides sufficient context for an agent to use the tool effectively.

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?

Schema description coverage is 100%, so baseline is 3. The description adds valuable tuning context for tree_depth and max_turns beyond the schema, explaining payload/size errors and result completeness. For other parameters, the schema already suffices. Thus, a 4 is warranted.

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 performs AI-driven semantic code search, returns file paths with line ranges and grep keywords. It distinguishes itself from the only sibling, extract_windsurf_key, which extracts a key.

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 includes a parameter tuning guide that explains when to adjust tree_depth and max_turns based on errors or shallow results. It also advises using the [config] line in responses for retry decisions. However, it does not explicitly state when not to use this tool, but given only one dissimilar sibling, this is acceptable.

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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