maid-runner-mcp

by mamertofabian

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.2.0
Disambiguation5/5
Each tool has a clearly distinct purpose: file tracking, stub generation, schema retrieval, initialization, manifest listing per file, snapshot of a single file, system-wide snapshot, and validation. There is no overlapping functionality.
Naming Consistency5/5
All tools follow the 'maid_' prefix with a verb_noun pattern (e.g., maid_generate_stubs, maid_list_manifests). The naming is consistent, descriptive, and predictable across all 8 tools.
Tool Count5/5
With 8 tools, the server is well-scoped for the MAID domain. It covers initialization, analysis, validation, stubbing, and tracking without being overly numerous or sparse.
Completeness4/5
The tools cover the core MAID workflow (init, snapshot, validate, generate stubs, track files, list manifests). However, there is no tool to list all manifests (only per file) or to delete/clean up manifests, which are minor gaps.
Average 4.5/5 across 8 of 8 tools scored. Lowest: 3.7/5.
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
- Last stable release on March 26, 2026
- 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?
Describes output as aggregated manifest using systemArtifacts array instead of expectedArtifacts object, and mentions return type. No annotations exist, so description carries burden; it does not disclose side effects, auth needs, or potential destructive actions, but given nature of tool, likely non-destructive.
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?
Well-organized with headings, bullet points, and sections. The Args section duplicates schema info without adding value, slightly reducing conciseness.
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 purpose, usage, and high-level output. Lacks parameter details and behavioral nuances. Output schema exists but not described, so completeness is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description should compensate. Args are listed with defaults but no additional explanation of formats, constraints, or how they affect behavior. Barely adds meaning beyond schema.
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 it generates a system-wide manifest snapshot. Use cases like documentation, architecture review, and dependency analysis distinguish it from siblings like maid_snapshot (per-file) and maid_list_manifests.
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 'When to use' section with three bullet points. Also includes tips for periodic runs and onboarding. Lacks explicit when-not-to-use, but usage context is clear.
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 the burden. It explains result categories (created_by, edited_by, read_by) but does not mention any side effects, permissions, or rate limits. For a read-only listing tool, this is acceptable but not thorough.
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 (about 10 lines) and well-structured with headings for purpose, usage, result categories, tips, and arguments. Every sentence adds value, and the most important information is front-loaded.
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 is reasonably complete for a list tool: it explains what the tool returns (ListManifestsResult) and categorizes results. The presence of an output schema is acknowledged. However, it could mention pagination or handling of missing files.
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?
The input schema has 0% description coverage, but the description's Args section adds brief but sufficient meaning: 'file_path: Path to the file to check' and 'manifest_dir: Directory containing manifests (default: manifests)'. This compensates for the schema gap.
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 'List manifests that reference a file', specifying both the action (list) and the resource (manifests referencing a file). This is distinct from sibling tools like maid_files or maid_validate, making it easy for an agent to select the correct tool.
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 provides explicit 'When to use' bullets covering before editing, understanding history, and planning edits. While it does not state when NOT to use or name specific alternatives, the context is clear and helpful for an 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?
No annotations are provided, so the description must disclose behavior. It states the return type (SchemaResult with manifest schema) but does not explicitly confirm it is read-only or safe. However, the tool's purpose implies no side effects, so the description is adequate but not 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?
The description is well-structured with clear sections for usage, key information, and tips. It is slightly longer than necessary but every sentence adds useful context. Front-loading with the main purpose is effective.
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's simplicity (no parameters, output schema exists), the description covers all necessary aspects: when to use, what the schema contains, and tips. It is complete for an agent to understand and invoke the tool correctly.
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?
The input schema has zero parameters, and the description provides no parameter details (none needed). According to the baseline rule for 0 params, the score is 4. The description adds value by explaining the return content and context.
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 retrieves the MAID manifest JSON schema, with a specific verb and resource. It distinguishes itself from siblings by explaining its role in understanding manifest structure before other operations like validation or creation.
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 lists when to use the tool in Phase 2 (Planning), debugging, and learning contexts. Although it doesn't state when not to use it, the context is clear and aligns with typical use cases for a schema retrieval tool.
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 are present, so the description carries full burden. It describes the return categories and suggests periodic use, implying a read-only non-destructive operation. However, it does not explicitly state whether the tool modifies any state or requires specific permissions, but the context suggests a safe query.
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 headers, bullet points, and an args list. It is concise yet informative, with no redundant sentences. Could be slightly tighter, but overall efficient.
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 moderate complexity (3 optional params, output schema exists), the description covers purpose, usage, parameters, and return values. It lacks explicit error handling or edge cases but is sufficient for an AI agent.
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?
With 0% schema description coverage, the description compensates by explaining all three parameters in the 'Args' section: manifest_dir, issues_only, and status. It provides defaults and the effect of each parameter, adding meaning beyond the schema types.
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 verb 'Get' and the resource 'file-level tracking status using MAID Runner'. The status categories are explained, distinguishing it from sibling tools like maid_list_manifests or maid_validate.
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?
Explicit 'When to use' section with specific scenarios (project health check, onboarding, compliance audit) plus tips on filtering. This provides strong guidance on when to use this tool versus alternatives.
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?
With no annotations, the description fully handles behavioral disclosure, detailing what it creates (directories), tips (run once), and return type. Could expand on potential side effects of force=True.
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 well-structured with clear sections, concise sentences, and no redundant information. Every sentence adds value.
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?
Despite no annotations and a low schema coverage, the description covers purpose, usage, effects, parameters, and return type comprehensively. Output schema exists but its absence in the prompt doesn't reduce completeness.
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 description coverage is 0%, but the description adds meaningful explanations for both parameters (target_dir, force) beyond the schema's defaults and types, aiding correct invocation.
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 initializes a MAID project, listing its verb-init, resource-project, and scope. It distinguishes from siblings by focusing on initialization vs. file listing or validation.
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 when-to-use scenarios (starting new project, onboarding, resetting) and when to use force=True. However, it doesn't explicitly mention when NOT to use it or compare with sibling tools.
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 describes what is generated (naming, class structure, imports, placeholder methods) and that the test file is automatically added to the manifest's readonlyFiles. Lacks details on error handling but is sufficiently transparent.
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?
Well-structured with clear sections (When to use, What it generates, Tips, Args, Returns). Every sentence adds value; no wasted words.
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 output schema exists, the description appropriately explains the generation outcome. The tool's single parameter is well-contextualized, and the usage guidance makes it easy for an agent to decide when to use this tool.
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 0%, but the description includes an 'Args' section that explains 'manifest_path: Path to the manifest JSON file', adding meaning beyond the schema's type-only definition.
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 'Generate test stubs from a manifest using MAID Runner.' It specifies the verb and resource, and the tool's purpose is distinct from sibling tools like 'maid_validate' or 'maid_snapshot'.
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?
Explicitly lists when to use (Phase 2 Planning, jumpstarting tests, consistency) and provides tips (run after creating/updating manifest). Differentiates itself well without needing to list exclusions.
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?
With no annotations, description discloses validation modes, behavior for editing files, and return type. Lacks mention of side effects, but validation is likely read-only. Good transparency overall.
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?
Well-structured with headings and bullet points, but some redundancy (e.g., 'Validation modes' could be tighter). Still concise and easy to scan.
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?
Covers all necessary aspects: purpose, usage guidelines, parameter explanations, and return value. With output schema present, the description is complete for an agent to use correctly.
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?
Schema description coverage is 0%, but the description explains each parameter: manifest_path, validation_mode (with options), use_manifest_chain, manifest_dir, quiet. Adds significant meaning beyond the schema.
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 'Validate a MAID manifest using MAID Runner', specifying the verb and resource. It distinguishes from siblings like maid_files and maid_init, which have 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance on when to use: Phase 2 and 3, before committing. Also covers validation modes and manifest chain usage, providing clear context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries full burden. It details key behaviors: analyzing source files, extracting artifacts, creating manifests, and optionally generating test stubs. This fully discloses the tool's actions.
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 well-structured with sections, bullet points, and a clear 'Args' list. Every sentence adds value, and the length is appropriate for the tool's complexity.
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 output schema exists, return values are briefly covered. The description covers usage, behavior, and parameters comprehensively. Minor omission: no mention of prerequisites (e.g., MAID Runner installation) or error handling, but overall complete for an agent to use correctly.
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 compensates fully by listing each parameter (file_path, output_dir, force, skip_test_stub) with clear explanations of their purpose and defaults. This adds significant meaning beyond the raw schema.
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 generates a manifest snapshot from existing code using MAID Runner. It specifies the resource (manifest snapshot) and action (generate), and differentiates from siblings like maid_snapshot_system by focusing on per-file snapshots.
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?
Provides an explicit 'When to use' section with three concrete scenarios (onboarding, refactoring, documentation) and 'Tips' with actionable advice. This gives clear guidance on when to invoke this tool versus alternatives.
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:

[![maid-runner-mcp MCP server](https://glama.ai/mcp/servers/mamertofabian/maid-runner-mcp/badges/card.svg)](https://glama.ai/mcp/servers/mamertofabian/maid-runner-mcp)

Score Badge

Copy to your README.md:

[![maid-runner-mcp MCP server](https://glama.ai/mcp/servers/mamertofabian/maid-runner-mcp/badges/score.svg)](https://glama.ai/mcp/servers/mamertofabian/maid-runner-mcp)

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/mamertofabian/maid-runner-mcp'

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