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SK-DEV-AI

codesearch MCP Server

by SK-DEV-AI

searchcode

Analyze a GitHub repository to inspect languages, complexity, and tech stack; search code, list files, read source, and find quality issues.

Instructions

Per-repo code intelligence via SearchCode (free, no auth). Use analyze for instant repo overview (languages, complexity, tech stack, credentials), search for searching code within a repo, findings for code quality issues, file_tree to list files, or get_file to read a file. No rate limits. e.g. searchcode(action='analyze', repository='https://github.com/expressjs/express')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoSubdirectory path filter (for analyze/findings/file_tree)
queryNoSearch query (for search action)
actionYesanalyze=repo overview, search=search code, findings=quality issues, file_tree=list files, get_file=read file
categoryNoFilter findings by category (for findings action)
end_lineNoEnd line (for get_file without symbol_name)
languageNoLanguage filter (for analyze action)
severityNoFilter by severity (for findings action)
repositoryNoGit URL or owner/repo — REQUIRED for every searchcode action (e.g. https://github.com/expressjs/express)
start_lineNoStart line (for get_file without symbol_name)
max_resultsNoMax results (for search/findings)
symbol_nameNoFunction/class name to extract (for get_file action)
detail_levelNoResponse verbosity (for analyze action)summary
context_linesNoContext lines around matches (for search action)
case_sensitiveNoCase-sensitive search (for search action)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It helpfully discloses that the service is free, requires no auth, and has no rate limits, which is useful context. However, it does not state that operations are read-only, describe return format or pagination, or explain failure modes.

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 front-loaded with the core purpose and then routes through the action modes compactly in a single sentence, followed by a short example. It is efficient, though the action enumeration is dense and could be slightly more scannable.

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?

For a 14-parameter tool with no output schema and no annotations, the description plus full schema coverage gives an agent enough to select an action and invoke it. The main remaining gap is that response shape and output behavior are not described, but the action routing and auth/rate-limit context cover most needs.

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 description coverage is 100%, so every parameter is already documented, including enums and defaults. The description adds only the example usage ('action=analyze', 'repository=...'), which repeats what the schema already conveys. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific capability ('Per-repo code intelligence via SearchCode') and enumerates the five action modes with their purposes. It is clear what the tool does, but it never distinguishes itself from sibling tools such as code_search, code_read, or analyze, which likely overlap.

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?

It gives explicit routing guidance for each action ('Use analyze for instant repo overview... search for searching code... findings for code quality issues...'), plus a concrete example. It lacks any when-not-to-use guidance or comparison against the overlapping sibling tools.

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