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stela-mcp

stela-mcp

by Sachin-Bhat
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

edit_file

Replace exact line sequences in text files within allowed directories. Preview changes with dry run and get a git-style diff of modifications.

Instructions

Make selective edits to a text file based on exact line matches (or whitespace normalized). Each edit replaces an existing sequence of lines (old_text) with new lines (new_text). Returns a git-style diff of the changes. Use dry_run to preview. Only works within allowed directories:

  • /app

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>",
      "edits": "<edits>"
    },
    "name": "edit_file"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesPath to the file to edit
editsYesList of edit operations
dry_runNoPreview changes without applying

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description discloses that edits replace exact line sequences, returns a git-style diff, and restricts to allowed directories. It does not mention potential side effects like file locking or permission requirements, but for a file editing tool this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (3 sentences), front-loaded with the core action, and every sentence adds value (edit mechanism, return value, dry_run, directory restriction). No fluff.

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?

Given 100% schema coverage and no output schema, the description explains the return type (git-style diff) and directory restrictions. It could mention error cases or handling of multiple edits, but it is complete enough for typical use.

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 coverage is 100%, so baseline is 3. The description adds meaning about matching strategy (exact or whitespace-normalized) and that newlines must be included in old_text and new_text, which goes beyond the schema's generic 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 it makes selective edits to a text file using exact line matches or whitespace-normalized matching, distinguishing it from write_file (which overwrites or appends) and read_file. The verb 'edit' and resource 'file' are specific, and the mechanism of replacing old_text with new_text is explained.

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 advises using dry_run to preview changes, which is practical. However, it does not explicitly state when not to use this tool (e.g., for creating new files) or compare to alternatives like write_file. The context is clear enough for an AI agent to infer appropriate use.

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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