batuta-mcp
Server Quality Checklist
A "release" on Glama is not the same as a GitHub release. To create a Glama release:
- if you haven't already.
- Go to the Dockerfile admin page, configure the build spec, and click Deploy.
- Once the build test succeeds, click Make Release, enter a version, and publish.
This process allows Glama to run security checks on your server and enables users to deploy it.
- Disambiguation5/5
Each tool has a clear, distinct purpose: checking overlaps, decomposing input, and scaffolding worktrees. No overlap or ambiguity.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern with snake_case, making them predictable and easy to understand.
Tool Count5/5Three tools is exactly right for the focused domain of plan decomposition and worktree setup. The scope is well-defined.
Completeness5/5The tool set covers the full workflow: validation (check), creation (decompose), and execution (scaffold). There are no obvious gaps for its stated purpose.
Average 3.3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 22 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.
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glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
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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?
With no annotations, the description must disclose behavioral traits. It says 'Pure check' indicating a safe, read-only operation, and 'Returns overlaps and ok' hints at output structure. However, it does not specify whether modifications occur (though it suggests none) or other behaviors like potential 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences with no filler. It front-loads the core purpose ('Pure check') and immediately defines the task. Every word is impactful with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides minimal context for a simple tool with one parameter and an output schema. It covers the core function and non-destructiveness but omits usage context, parameter details, and how to interpret results beyond 'overlaps and ok.' Given the output schema exists, return values are partially covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain parameters. It mentions 'plans' but does not describe the inner fields branchSlug or fileBoundaries, leaving the agent without needed context about what constitutes a plan or how boundaries are structured.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'do any plans share file boundaries?' which clearly identifies the tool as an overlap checker. It uses the verb 'check' and resource 'plans' with 'file boundaries,' distinguishing it from sibling tools like decompose_into_plans and scaffold_worktrees that likely create or manage plans.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description says 'Pure check' implying it's non-destructive, but it does not explain context, prerequisites, or suggest when to use other tools. An agent would not know when to choose this over sibling tools.
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?
With no annotations, the description carries full burden. It discloses key behavior: 'auto-corrects overlaps once' and output fields ('Returns plans, overlapsResolved, attempts'). However, it omits side effects (e.g., does it create files?), permission needs, or idempotency, leaving some ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys purpose, constraints (2-5 plans, disjoint boundaries), behavior (auto-corrects), and output structure. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters and an output schema, the description mentions the output fields but lacks parameter explanations and usage guidelines. It is adequate but leaves gaps for the agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, yet the description adds no explanation for the three parameters ('brainDump', 'projectHint', 'repoPath'). The minimal value is provided by the schema names and types, but the description fails to clarify meaning or usage beyond that.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Split a brain dump into 2-5 plans with DISJOINT file boundaries (auto-corrects overlaps once).' The verb 'split' and resource 'brain dump into plans' is specific. It distinguishes from siblings like 'check_boundary_overlaps' and 'scaffold_worktrees' by focusing on decomposition with boundary handling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide when to use this tool versus its siblings. It implicitly assumes the user wants decomposition, but no explicit guidance on context, prerequisites, or situations to avoid is given.
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 are provided, so the description carries full burden. It discloses validation (git repo, no overlaps, safe paths) and creation, plus dry-run preview, but lacks details on error handling, state changes, or output specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences, front-loading the main actions. However, it could separate validation details for better structure; overall it efficiently conveys key points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (nested plans array, validation steps) and lack of parameter descriptions, the description is moderately complete. The presence of an output schema partially compensates, but details on validation criteria and parameter constraints are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 mentions 'per plan' and 'dryRun', but does not explain the individual fields of the plans array (title, specMd, fileBoundaries, branchSlug), leaving parameter semantics incomplete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates and creates worktrees per plan, returning a ready-to-paste prompt. This distinguishes it from sibling tools 'check_boundary_overlaps' and 'decompose_into_plans', which handle earlier steps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is used after planning and overlap checking, but doesn't explicitly state when to use it or when not to, nor does it mention alternatives beyond the implied workflow.
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.
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