mastr-mcp-server

by UliRCS

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

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Latest release: v1.0.1
Disambiguation5/5
Each tool targets a distinct entity type (power generation, actors, consumption, gas production, gas consumption, grid connections) and the connection test. No overlap in purpose; descriptions clearly differentiate them.
Naming Consistency4/5
All search tools follow a consistent 'search_{domain}_public' pattern. The lone 'get_local_time' breaks the pattern but serves a distinct health-check purpose, so the deviation is minor and acceptable.
Tool Count5/5
With 7 tools, the set covers the major queryable entities in MaStR without being excessive. Each tool serves a clear role, and the count is appropriate for the server's scope.
Completeness3/5
The tools cover searching for all main entity types, but lack retrieval of full details (e.g., get_unit, get_grid_connection) after search, requiring external SOAP calls. This is a notable gap for a complete workflow.
Average 4.2/5 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
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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"
  ]
}
```
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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?
No annotations are provided, so the description carries the full burden. It states 'no auth required', mentions 23 filter keys, and notes the special handling of '&' characters. However, it does not disclose pagination behavior, rate limits, or error responses, leaving some 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 concise and well-structured. It front-loads the purpose, uses bold for emphasis, and employs a clear note format. Every sentence adds necessary information 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?
Given the lack of an output schema, the description does not explain return values. It mentions 23 filter keys but not their full meaning (schema does). Pagination limits are partially covered. For a public search tool, more detail on response structure would improve 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 coverage is 100%, so the schema documents all parameters. The description adds value by explaining the operator suffixes for filters, dropdown field acceptance of labels/IDs, boolean field formats, and a notable tip about searching with '&' in company names. This goes 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 the tool searches for market actors (Marktakteure) and lists examples like DSOs, generators, suppliers. It distinguishes from sibling tools that search other specific domains (power generation, consumption, gas, etc.).
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 finding energy market participants but does not explicitly compare to siblings or provide when-not-to-use scenarios. The context of sibling tool names gives some guidance, but the description itself lacks direct usage guidance.
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 provided, the description discloses 'no auth required' and that it searches gas consumers, but it omits behavioral traits such as pagination, return format, or idempotency. It does not specify whether the tool is read-only or describe any side effects.
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 with three sentences: a bold statement of purpose, a short clarifying line, and a list of filter categories. It is front-loaded with key information and contains no unnecessary words.
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?
Given the complexity of the tool (3 parameters, nested filters, no output schema), the description lacks information about what the search returns (e.g., fields of results), pagination details beyond what the schema provides, and usage boundaries. It covers the core purpose and filter categories but is not fully complete.
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 description coverage is 100%, so the schema already documents parameters thoroughly. The description adds some context by highlighting filter categories and giving examples, but it does not significantly augment the schema's parameter documentation.
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 searches for gas consumption units (Gasverbrauch) with 'Search' as the verb and specifies the resource as 'gas consumption units' (gas consumers). It distinguishes itself from siblings like search_power_consumption_public and search_gas_production_public by explicitly targeting gas consumption.
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 indicates the tool requires no authentication and is for finding registered gas consumers, which implies its appropriate context. However, it does not provide explicit when-to-use or when-not-to-use guidance or mention alternative 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?
No annotations provided. Description discloses 'no auth required', return data types, and supported connection types. However, misses details on rate limits, pagination behavior, error handling, or response structure.
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?
Description is well-structured with bolded main point, clear paragraphs for returns and types, and efficient one-sentence alternative guidance. No unnecessary words.
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 no output schema, description adequately covers return data types and connection types. Could improve by detailing output structure or field examples, but overall sufficient.
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%, baseline 3. Description adds significant value beyond schema by explaining filter operators, shared vs type-specific keys, boolean 'planned', and dropdown field format (label/ID).
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 searches grid connection points and locations, using specific verb 'Search' and resource 'grid connection points and locations'. It distinguishes from sibling tool get_grid_connection for specific unit details.
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 alternative tool (get_grid_connection) for specific unit details. Does not directly compare with sibling search tools like search_power_generation_public, but implies this tool is for connection points across all types.
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 states 'no auth required' and implies a read-only search, but with no annotations, the burden is on the description. It does not explicitly declare read-only, rate limits, or other behavioral traits beyond auth. Adequate but could be more 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?
The description is concise (around 100 words), well-structured with bullet points, and front-loads the purpose. 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex search tool with many filter keys and operators, the description covers the essentials: filter syntax, supported keys, tech shortcuts, and dropdown fields. No output schema is provided, but the tool is a search that likely returns a list of units; the description is adequate.
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%, but the description adds significant value: it explains filter operators, provides examples, lists tech shortcuts, and mentions German aliases and dropdown field behavior. This goes beyond the schema 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 'Search power generation units' and lists specific energy carriers (wind, solar, biomass, etc.), distinguishing it from sibling search tools (e.g., search_actors_public). The verb 'search' and resource 'power generation units' are explicit.
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 'Use this as the default search tool for power generation questions' and directs users to get_unit for full details after finding a MaStR number. It provides clear context but does not explicitly exclude other search tools for power generation.
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 covers key behavioral aspects: it is a read-only public search (no auth), details filter operators and dropdown fields, and provides an example. It does not mention rate limits or pagination behavior beyond default page size, but these are less critical for search.
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 efficient: two short paragraphs, bolded key phrase, and bullet-like listing. Every sentence adds value without redundancy, making it easy to scan.
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 tool has no output schema and nested objects. The description focuses on input parameters and search scope but does not explain return fields or structure. For a search tool, this is adequate but could mention typical response fields (e.g., unit details, capacity).
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 100% description coverage with each parameter documented. The description adds significant value by listing filter key categories (gas technology, unit type) and explaining operators, dropdown values, and an example, greatly enhancing understanding 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 it searches gas production and storage units (Gaserzeugung), listing specific types like biogas, Power-to-Gas, LNG, fossil gas, and storage, and explicitly says no auth required. It distinguishes from sibling tools like search_power_generation_public by targeting gas-specific entities.
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 indicates it is for public data (no auth) and lists the types of units, implying use for gas-related searches. However, it does not explicitly state when not to use it or provide direct comparisons to siblings, but the context from sibling names (power, actors, consumption) gives 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?
Describes the tool as a test returning server time with no authentication, which sufficiently discloses its behavior given its simplicity.
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, front-loaded with purpose, 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?
For a simple test tool with no parameters and no output schema, the description is fully adequate.
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?
No parameters exist, and description adds nothing beyond schema, but baseline of 4 is appropriate for zero-parameter tools.
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 is a connection test that returns server time, distinguishing it from sibling search tools.
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 says 'Use this to verify network connectivity' and notes no authentication required, providing clear context for use.
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?
The description discloses that no authentication is required, that it finds large consumers in MaStR, and lists 29 filter keys across categories. Since no annotations are provided, the description carries full burden and does well, though it could mention return format or data freshness.
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 with three short paragraphs: purpose/auth, details, usage guidance. Information is front-loaded and every sentence earns its place.
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 no output schema, the description could mention what the response contains. It does state it finds large consumers, but lacks specifics. Otherwise, it covers all input parameters and usage context well.
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%, baseline 3. The description adds value by explaining filter operator suffixes, listing all filter keys, and providing an example. This goes beyond the schema descriptions, which are brief.
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 searches for power consumption units (electricity consumers) and distinguishes from generators. It specifies the resource type and verb, and the context signals include a sibling tool for generation, making the purpose unambiguous.
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 searching for electricity consumers (not generators),' providing a clear directive and pointing to the sibling tool for generation. This is excellent guidance.
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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