MCP Migration Advisor

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.2.15
Disambiguation5/5
Each tool has a distinct purpose: analyze different input formats, score risk, generate rollback, and detect conflicts. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with clear verb_noun structure (e.g., analyze_migration, generate_rollback), making them predictable for an agent.
Tool Count5/5
With 6 tools, the server is well-scoped for a migration advisor. Each tool covers a necessary aspect without redundancy or excess.
Completeness4/5
The tools cover analysis, scoring, rollback generation, and conflict detection for SQL and Liquibase formats. A minor gap is the lack of a tool for overall migration order validation or automatic fixing, but the core advisory workflow is complete.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, description provides a detailed list of detection patterns (e.g., ACCESS EXCLUSIVE locks, data loss operations, cascade risks) but omits behavioral traits like processing limits, side effects (assumed read-only), or performance implications. Sufficient for basic understanding but could be more thorough.
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?
Description is concise and front-loaded with the core purpose, then lists specific patterns in bullet-like format. Each sentence adds value; no fluff. Could be slightly more structured (e.g., spacing) but largely efficient.
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?
As a static analysis tool without an output schema, the description should clarify what the tool returns (e.g., list of risks, severity scores). It currently lists what it detects but not the response format, leaving agents unsure of how to interpret output.
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?
Input schema has 100% description coverage for both parameters (filename, sql). Description adds context about filename patterns (V__*.sql, R__*.sql) beyond schema, but this is marginal. Baseline 3 is appropriate given high schema coverage.
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?
Description clearly states it analyzes SQL migration files for lock risks, data loss, and unsafe patterns. Lists specific detectable operations (e.g., ACCESS EXCLUSIVE locks, data loss operations) and distinguishes from sibling tools (analyze_liquibase, analyze_liquibase_yaml) by specifying Flyway support.
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?
Explicitly states support for Flyway versioned and repeatable migrations, and plain SQL files, which guides when to use this tool. However, lacks explicit when-not-to-use or direct comparison to siblings, though naming and context provide implicit differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must fully disclose behavior. It mentions generating rollback SQL with warnings for irreversible operations and Flyway schema_history cleanup. However, it does not clarify execution side effects (e.g., does it apply changes or only produce text?) or authentication requirements, leaving some behavioral gaps.
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 extremely concise at two sentences, with no filler. It is front-loaded with the primary action ('Generate reverse DDL') and then adds key details, making it easy for an agent to parse quickly.
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?
Without an output schema, the description partially compensates by stating the tool produces rollback SQL with warnings and Flyway cleanup. However, it does not describe the format of the output or how warnings are presented, leaving some ambiguity for an agent expecting a structured response.
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?
The input schema has 100% coverage with descriptions for both 'filename' and 'sql'. The description adds no additional meaning beyond the schema examples (e.g., filename format is already illustrated). Thus, it meets the baseline for high schema coverage but does not compensate further.
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: 'Generate reverse DDL to undo a SQL migration.' It specifies the output (rollback SQL with warnings for irreversible operations) and mentions Flyway cleanup, distinguishing it from sibling analysis tools like analyze_migration and detect_conflicts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when undoing a migration but does not explicitly state when to use this tool versus alternatives, nor does it provide 'when not to use' guidance. It lacks explicit comparison with sibling tools, leaving room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description reveals behavioral traits well: it details the analysis scope (lock risks, data loss, unsafe patterns) and specifies lock types (ACCESS EXCLUSIVE, SHARE). However, it does not explicitly state that the tool is read-only and has no side effects, which would be helpful.
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 and well-structured: it front-loads the primary purpose, lists supported change types in a bullet-like manner, and mentions output details. Every sentence adds value without redundancy.
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?
The description is fairly complete for a simple tool with one parameter and no output schema. It covers input, analysis scope, and types of returns (lock risk severity, data loss risk). However, it lacks detail on the output format or structure, which is necessary since there is no output schema.
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 coverage is 100%, so the schema already describes the 'xml' parameter. The description reinforces that the input is a Liquibase XML changelog but adds no new semantic details beyond what the schema provides. Baseline score is appropriate.
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: 'Analyze a Liquibase XML changelog for lock risks, data loss potential, and unsafe patterns.' It lists supported change types and inline SQL analysis, making it specific and distinct from siblings like analyze_liquibase_yaml.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied (use when analyzing Liquibase XML changelogs), but no explicit guidance on when not to use it or how to choose among siblings like analyze_migration or analyze_liquibase_yaml. This leaves ambiguity for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool aggregates lock risk and data loss potential into a score, but does not explicitly state that it is read-only or has no side effects. Since no annotations are provided, the description carries full burden; it is adequate but lacks explicit safety information.
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 two sentences long, front-loading the purpose and output range in the first sentence, and elaborating on the components and use case in the second. No redundant information.
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?
The description explains the output (0-100 score) and what it comprises, which is sufficient for an agent to understand the return value. It could provide more detail on interpretation, but given the simplicity and no output schema, it is complete enough.
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?
Both parameters (filename and sql) have descriptions in the input schema with 100% coverage. The tool description does not add further meaning to these parameters beyond what the schema provides, so baseline score of 3 is appropriate.
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 calculates a combined risk score (0-100) for SQL migrations, specifying the aggregated factors (lock risk severity and data loss potential). This distinguishes it from sibling tools like analyze_migration or detect_conflicts, which serve different purposes.
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 mentions the tool is useful for CI gates and automated migration review pipelines, providing context on when to use it. However, it does not explicitly state when not to use it or mention alternatives, though sibling tools cover related but distinct tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description fully discloses behavioral traits: it analyzes for lock risks, data loss, and unsafe patterns, and returns severity levels. It does not explicitly state that it is read-only, but the analysis nature implies no side effects, which is sufficient for transparency.
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 and well-structured: three sentences front-loaded with purpose, supported features, and outputs. Every sentence adds value without redundancy.
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 the tool's simplicity (single parameter, no output schema, no annotations), the description covers purpose, supported change types, and outputs. It could mention output format or limitations (e.g., only YAML) but is sufficiently complete for the complexity.
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?
With 100% schema description coverage, the schema already describes the single parameter 'yaml' as 'The Liquibase YAML changelog content'. The description adds context about supported change types but no additional semantic meaning for the parameter itself, so baseline of 3 is appropriate.
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: analyzing Liquibase YAML changelogs for lock risks, data loss potential, and unsafe patterns. It lists specific supported change types and inline SQL checks, which distinguishes it from sibling tools like analyze_liquibase (likely for XML) or analyze_migration (broader scope).
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 context is clear: use this tool when you have a Liquibase YAML changelog to analyze. However, it does not explicitly state when not to use it or suggest alternatives among sibling tools, so it misses some guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains what conflicts are detected (structural) and what are not (semantic), and mentions lock contention and drop dependencies. However, it does not describe output format or performance characteristics, leaving minor gaps.
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?
Two sentences: first defines purpose, second gives usage and limitation. No unnecessary words. Every sentence adds value.
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 sibling tools complexity and no output schema, the description is fairly complete. It specifies scope and limitations. However, it lacks any indication of what the tool returns, which would be helpful for a conflict detection tool.
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 coverage is 100%, so baseline is 3. The description does not add new parameter meaning beyond the schema; it merely states the tool's purpose which implies the parameters are migration files. No additional syntax or format details are provided.
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 detects structural conflicts between two SQL migration files, listing specific types (same-table, same-column, lock contention, drop dependencies). It differentiates from siblings like analyze_migration by focusing on conflict detection between two files.
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 explicitly says 'Use this when two migrations touch the same schema objects and you need to know if ordering or concurrent execution matters.' It also notes a limitation: semantic conflicts like different indexes are not reported, providing when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![mcp-migration-advisor MCP server](https://glama.ai/mcp/servers/Dmitriusan/mcp-migration-advisor/badges/card.svg)](https://glama.ai/mcp/servers/Dmitriusan/mcp-migration-advisor)

Score Badge

Copy to your README.md:

[![mcp-migration-advisor MCP server](https://glama.ai/mcp/servers/Dmitriusan/mcp-migration-advisor/badges/score.svg)](https://glama.ai/mcp/servers/Dmitriusan/mcp-migration-advisor)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Dmitriusan/mcp-migration-advisor'

If you have feedback or need assistance with the MCP directory API, please join our Discord server