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agent-lsp

agent-lsp

by blackwell-systems
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Hybrid

apply_edit

Apply file edits using either workspace edits from rename/format operations or text matching with whitespace normalization for accurate replacements.

Instructions

Apply an edit to a file. Two modes: (1) WorkspaceEdit mode — pass workspace_edit with positional changes returned by rename_symbol or format_document; (2) Text-match mode — pass file_path + old_text + new_text to find and replace text without needing line/column positions. Text-match tries exact match first, then whitespace-normalised line match (handles indentation differences). Use text-match when AI-generated positions would be imprecise.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "apply_edit"
  }
}

Input Schema

NameRequiredDescriptionDefault
new_textNo
old_textNo
file_pathNo
workspace_editNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.16.0
  2. Removedv0.15.0
  3. Addedv0.11.1
  4. Removedv0.10.0
  5. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations provided, so the description carries the burden. It explains text-match tries exact match then whitespace-normalized line match, but lacks details on mutation, error handling, return values, or safety implications.

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?

Three well-structured sentences, front-loaded with purpose and modes. No redundant information, though the third sentence could be slightly tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers modes and text-match behavior, but given 4 parameters and no annotations or output schema, it lacks details on failure cases, response format, or prerequisites like file existence.

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 0%, so the description must compensate. It clarifies that workspace_edit is used in one mode and file_path + old_text + new_text in the other, but does not fully specify formats or constraints, leaving some ambiguity.

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?

Clearly states the tool applies an edit to a file and distinguishes two specific modes (WorkspaceEdit and Text-match). The description ties each mode to its use case, making it highly clear what the tool does.

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?

Provides explicit guidance on when to use each mode: WorkspaceEdit for positional changes from rename_symbol/format_document; Text-match when AI-generated positions are imprecise. Does not list alternatives or exclusions but gives practical context.

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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