Maango-mcp

Official

by maango-io

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Tools are largely distinct with clear purposes, but the three lookup_domain variants (summary, full, conflicts) could be confused if descriptions are not read carefully. batch_check and check_permission are clearly separate from the lookup family.
Naming Consistency4/5
Most tools follow a verb_noun snake_case pattern (lookup_domain, get_changelog, check_permission, search_domains). batch_check deviates slightly as noun_verb, but the naming remains readable and predictable overall.
Tool Count5/5
Seven tools is well within the ideal range for a registry query service. Each tool fulfills a distinct query need with no unnecessary overlap or bloat.
Completeness5/5
The server covers the full spectrum of registry queries: single-domain summary, full raw data, conflicts, permission checking, batch comparison, domain search, and change history. There are no obvious gaps for a read-only policy registry.
Average 4.3/5 across 7 of 7 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
- Last stable release on May 5, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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 carries the transparency burden. It discloses the output behavior ('Returns any conflicting signals') and gives an illustrative example. However, it does not explicitly state read-only safety, permissions, or other operational characteristics, though 'Get' implies a safe read.
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 compact and front-loaded with the purpose. It includes a brief explanatory sentence and a straightforward args list, with no wasted words or unnecessary detail.
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?
For a simple one-parameter lookup tool, the description covers purpose, output meaning, and parameter example. It lacks explicit usage guidelines, but that gap is already captured elsewhere. Overall, it is complete enough for correct invocation.
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 single parameter 'domain' is explained with a concrete format and example ('e.g. "nytimes.com"'), adding semantic meaning that the input schema lacks (0% coverage). This adequately compensates for the schema's bare string type.
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 resource ('policy conflicts for a domain from the Maango registry'), and it differentiates from siblings like lookup_domain and lookup_domain_full by focusing specifically on conflicting policy signals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternative lookup tools. The description implies conflict checking but does not explicitly state selection criteria or mention alternative approaches.
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 that the tool returns recent policy changes and supports pagination via limit/offset, but it does not mention ordering, time range, or explicitly state that it is a read-only operation. These details are left to the user's inference.
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 compact and well-structured: a clear opening sentence, a brief summary sentence, and a focused parameter list. Every part earns its place, and the front-loaded verb phrase immediately establishes the tool's purpose.
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?
With four optional parameters and an output schema present, the description covers the key aspects of filtering and pagination. It does not explicitly discuss when to use this tool versus siblings, but the name and description make that reasonably clear. Minor omission of output-related context is acceptable given the output schema.
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?
Since the input schema has 0% description coverage, the description fully compensates with an Args section that explains every parameter: domain with an example, change_type with a complete list of allowed values, limit with a valid range and default, and offset with its purpose. 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 opens with 'Get AI policy change history from the Maango registry,' which is a specific verb+resource. It clearly distinguishes this tool from siblings like lookup_domain and search_domains by focusing on change history across domains.
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 explains what the tool does and how to filter by domain and change type, but it does not explicitly mention when to use this tool over alternatives or provide any exclusions. Usage context is implied but not directly contrasted 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 are provided, so the description carries the full burden. It discloses the tool's output behavior comprehensively by enumerating returned content (AI stance, per-use-case policies, bot lists, signal presence, site metadata). However, it omits details on error handling, authentication, or rate limits.
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: one sentence for purpose, a bullet-like list of return items, and a parameter specification. Every sentence adds value with no redundancy or 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?
For a single-parameter lookup with an output schema, the description is fairly complete: it explains the purpose, parameter, and return content in detail. While it lacks usage context relative to siblings, that gap is already reflected in the usage_guidelines score.
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 provides a dedicated 'Args' section defining 'domain' with a format example ('nytimes.com'). This fully compensates for the lack of schema descriptions, giving clear semantic meaning to the single parameter.
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 function: 'Look up a domain's AI policy summary from the Maango registry.' The verb 'look up' and resource 'domain's AI policy summary' are specific, and the use of 'summary' implicitly distinguishes it from the sibling tool 'lookup_domain_full'.
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 for retrieving a summary for a single domain, but provides no explicit guidance on when to use this tool versus siblings like lookup_domain_full, search_domains, or check_permission. No alternative tools or exclusions are mentioned.
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 takes on the burden of disclosing behavior. It enumerates the returned data fields (robots.txt, ai.txt, llms.txt, TDM-Rep, crawl rules, meta tags, content signals), giving the agent a solid mental model. It doesn't mention side effects or failure modes, but as a read-only lookup, the disclosure 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 compact and well-structured. The first sentence states the purpose, the second lists return categories, and an 'Args' section handles the parameter. Every sentence earns its place with no redundancy or wordy filler.
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 covers the tool's purpose, output contents, and the lone parameter. An output schema exists, which handles return-value structure details, so the description doesn't need to over-explain. It lacks edge-case information, but for a detailed lookup tool with rich output schema, it is sufficiently complete.
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 only a 'domain' property with no description. The tool description compensates by defining it as 'The domain to look up' with an example ('nytimes.com'). This adds meaning beyond the schema, though it doesn't specify constraints like whether subdomains are accepted or if a protocol is needed, leaving slight 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?
The description starts with a clear verb and resource: 'Get full raw policy data for a domain from the Maango registry.' It distinguishes itself from the sibling lookup_domain by explicitly stating it is 'much more detailed', leaving no ambiguity about its specific 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 description implies when to use this tool by emphasizing its comprehensive detail and contrasting it with lookup_domain. However, it doesn't explicitly state 'use this when you need full data' as a directive or list exclusions, so it falls just short of fully explicit usage 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 are provided, so the description carries the transparency burden. It discloses meaningful behavioral details: prefix matching, a 2-character minimum, accepted stance values, limit range 1-100, default 20, and offset pagination. It does not mention case sensitivity or result ordering, but the provided details are sufficient for safe use.
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 compact and well-structured: a one-sentence purpose, a one-sentence return summary, and a terse Args block. Every sentence adds useful information, and there is no redundant or filler content.
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?
With four parameters, an output schema, and no annotations, the description covers all necessary invocation knowledge: purpose, return content, parameter meanings, constraints, and defaults. The output schema handles return structure, so no further explanation of return values is needed.
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?
The schema has 0% description coverage, but the description's Args section adds thorough semantics for every parameter: query is a prefix with example and min length, stance has explicit allowed values, limit has a range and default, and offset has a default. This exceeds what the bare input schema provides.
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 opens with a specific verb and resource: 'Search for domains in the Maango AI policy registry by prefix.' It also states what is returned ('matching domains with their stance and Tranco rank'), which clearly distinguishes prefix search from exact-match sibling tools like lookup_domain.
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 use case is implied by 'by prefix' and the parameter details, but there is no explicit when-to-use or when-not-to-use guidance, nor any named alternative such as lookup_domain for exact matches. The agent must infer that prefix search is intended for partial/unknown domain names.
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 provided, so the description carries the full disclosure burden. It states that it returns each domain's stance, use-case policies, and bot lists side-by-side, and also communicates the 2-25 domain constraint. Since this is a read-only lookup operation, the lack of side-effect warnings is acceptable, though it does not explicitly state read-only.
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 a short paragraph followed by a well-structured Args block. It front-loads the primary purpose in the first sentence and uses minimal, high-value wording for the parameter explanation.
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 single parameter and the presence of an output schema, the description provides sufficient context for an agent to select and invoke the tool correctly. The 2-25 range and example ensure valid input, and the return description clarifies what to expect.
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?
The schema only defines 'domains' as an array of strings, but the description's Args section adds crucial semantics: it defines the required count (2-25), provides a concrete example, and clarifies that the entries are domain names. This fully compensates for the 0% schema description 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?
The description opens with 'Compare AI policies across multiple domains using the Maango registry', which clearly states the verb (compare), resource (AI policies), and scope (multiple domains). It also mentions the batch nature (2-25 domains) and differentiates from single-lookup siblings like lookup_domain.
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?
It explicitly instructs the agent that this is for comparing 2-25 domains at once, making it obvious when to use it. However, it does not explicitly name alternatives like 'use lookup_domain for a single domain', so it lacks formal exclusions.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It fully discloses the return structure (allowed bool, reason code, explanation, domain stance, signals), enumerates all reason codes and their meanings, explains the bot_blocked condition, and clarifies that no_policy/unspecified result in block behavior. This is exemplary transparency.
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 longer than average but well-structured with a lead sentence, a return summary, a reason-code list, and an Args section. Each section earns its place; the reason-code enumeration is somewhat lengthy but valuable for interpreting results. It is appropriately sized for the tool's complexity.
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?
The tool has three parameters, an output schema, and requires nuanced policy handling. The description covers the input format, allowed actions, return values, and all possible decision outcomes, including error cases. There is no significant missing context that an agent would need to invoke this 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?
Although the schema has no descriptions (0% schema coverage), the description thoroughly explains all three parameters: domain with an example, action with an explicit enum-like list, and agent_id with its optionality and behavioral effect (bot_blocked). It adds far more meaning than the bare schema, compensating completely.
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 uses a specific verb ('Check whether') and names the exact resource ('a specific action on a given domain'), and it clearly differentiates itself from sibling lookup tools by focusing on AI-agent permission decisions. It even enumerates the exact actions covered (scrape, summarize, train, search), making the purpose unmistakable.
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 guidance: 'Use this before your agent scrapes, summarizes, trains, or searches content from a site.' It clearly states when to invoke the tool, though it doesn't explicitly name alternatives for cases where a plain domain lookup would suffice. Still, the intended usage context is well defined.
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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