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Jamie-BitFlight

git-project-xray-mcp

find_symbol

Locate functions, classes, or methods in a codebase using fuzzy matching. Provide a root path and a query to get symbol details including name, type, file path, and line numbers.

Instructions

🔍 STEP 2: Find specific functions, classes, or methods in the codebase.

USE THIS AFTER explore_repo() when you need to locate a specific piece of code. Uses fuzzy matching - you don't need the exact name!

INPUTS:

  • root_path: Same ABSOLUTE path used in explore_repo

  • query: What you're looking for (fuzzy search works!) Examples: "auth", "user service", "validate", "parseJSON"

EXAMPLE INPUTS: find_symbol("/Users/john/awesome-project", "authenticate") find_symbol("/Users/john/awesome-project", "user model") # Fuzzy matches "UserModel"

EXAMPLE OUTPUT: [ { "name": "authenticate_user", "type": "function", "path": "/Users/john/awesome-project/src/auth.py", "start_line": 45, "end_line": 67 }, { "name": "AuthService", "type": "class", "path": "/Users/john/awesome-project/src/services.py", "start_line": 12, "end_line": 89 } ]

RETURNS: List of symbol objects (dictionaries). Save these objects - you'll pass them to what_breaks()! Empty list if no matches found.

WHAT TO DO NEXT: Pick a symbol from the results and pass THE ENTIRE SYMBOL OBJECT to what_breaks() to see where it's used in the codebase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
root_pathYes
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description fully discloses behavior: uses fuzzy matching, returns a list of symbol objects with exact fields, empty list if no matches, and how to proceed with the output. No annotations exist, so the description carries the full burden well.

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 clear sections (STEP, INPUTS, EXAMPLES, RETURNS, NEXT) and front-loaded with purpose. It is somewhat verbose but every sentence adds value, though could be slightly more concise.

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 the tool has only 2 parameters and an output schema, the description is very complete. It explains the return format via example, the usage flow, and links to sibling tools, leaving no ambiguity.

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?

With 0% schema description coverage, the description adds critical meaning: root_path must be the same absolute path from explore_repo, query examples are given. This compensates for the schema's lack of descriptions.

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's purpose: finding specific functions, classes, or methods in the codebase. It specifies the verb 'find' and the resource 'symbols', and distinguishes from siblings like explore_repo and what_breaks.

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?

The description provides explicit when-to-use guidance: 'USE THIS AFTER explore_repo()' and 'Pick a symbol from the results and pass... to what_breaks()'. It also mentions fuzzy matching and gives practical examples.

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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