Brewfather MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource or operation: batches, recipes, readings, inventory, efficiency analysis, comparison, and connection check. There is no overlap in purpose, so an agent can easily select the right tool.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case: list_*, get_*, analyze_*, compare_*, check_*. Even get_batch_readings fits the pattern with a compound noun, maintaining uniformity.
Tool Count5/5
Nine tools is well-scoped for a brewing data server. It covers core resources (batches, recipes, inventory) plus analysis and diagnostics without overloading the surface.
Completeness4/5
The tool set fully covers read-only access to batches, recipes, readings, inventory, and provides useful analytical features. The only gaps are write operations (create/update/delete) and a direct recipe search, but these are not essential for a read-focused integration.
Average 4.6/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 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.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint), the description discloses that there is no server-side name search, the filter is applied client-side, and it may return fewer results than limit. This is valuable behavioral context not covered by annotations. The return format is also specified.
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: a brief one-line purpose, a relevant note, and clear Args/Returns sections. Every sentence provides useful information without unnecessary elaboration.
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 list tool, the description is complete. It covers parameters, return fields, and a critical behavioral caveat. The output schema existence and annotations further complete the picture, so no essential information is missing.
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 input schema provides only titles and defaults, with no descriptions. The description's Args section fully explains both parameters: limit as 'Maximum recipes to fetch before filtering' and name_contains as 'Case-insensitive substring filter applied client-side.' This adds complete 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 'List saved recipes' with a specific verb and resource. It distinguishes from sibling tools by targeting recipes specifically, while the note about client-side filtering adds additional clarity about the tool's scope.
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?
The description provides no explicit guidance on when to use this tool versus alternatives like get_recipe or list_batches. The note about raising limit is a parameter usage tip, not a tool-selection guideline. There is no mention of when not to use it.
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?
Annotations already declare read-only/idempotent/non-destructive behavior. The description adds valuable context about large payloads and the ability to trim via sections, as well as the return structure including dual units.
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 efficiently structured with a purpose statement, Args/Returns sections, and no wasted words. It front-loads the key information and includes necessary detail.
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 lacking an explicit alternative mention, the description is complete for a read-only fetch tool: it explains purpose, parameters, return content, and payload-size implications, with annotations covering safety.
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 coverage is 0%, but the description fully documents both parameters: batch_id as 'from list_batches' and sections with a complete list of valid values and the default behavior of returning everything when omitted.
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 'Fetch one batch in full, or just the sections you need,' using a specific verb and resource, and distinguishes from sibling tools by emphasizing full vs partial retrieval and payload trimming.
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?
Implied usage is clear: use this to retrieve a specific batch with optional section filtering. However, it does not explicitly mention alternatives like get_batch_readings for measurements or list_batches for finding IDs, so it lacks explicit when-not or alternative 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?
Annotations already declare read-only and idempotent hints, and the description adds useful behavioral context by detailing the return content (grain bill percentages, hop schedule, mash/fermentation steps, target figures, dual unit systems) and the effect of omitting sections. This goes beyond the annotations without contradicting them.
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 tightly structured with Args and Returns sections, using only essential sentences. Every line adds value, from the purpose statement to parameter details and return summary.
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 rich annotations and an output schema present, the description still provides complete context: it covers both parameters, return content, and section selection. No critical information is missing for a simple read-only fetch operation.
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 zero descriptions, so the description fully compensates by clearly explaining recipe_id (source from list_recipes) and sections (allowed values and default behavior). This adds substantial meaning beyond the bare 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 tool's function with a specific verb ('Fetch') and resource ('one recipe'), and distinguishes it from siblings like list_recipes by focusing on fetching a single recipe with optional section filtering.
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 by noting recipe_id comes from list_recipes, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusion criteria. The 'sections' parameter hints at selective fetching, but usage context is largely inferred.
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?
Annotations already declare readOnly and idempotent safety. The description adds valuable behavioral details: row alignment by ingredient/step name, explicit nulls for non-used items, and return of totals where meaningful. No contradiction with annotations.
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-organized into purpose, args, and returns. The first sentence immediately states the function, and all information is directly useful with no padding.
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 simplicity of the tool (2 parameters, no nested objects) and the availability of an output schema, the description is complete. It explains the alignment behavior, parameter ranges, and return format sufficiently for an agent to invoke the tool 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?
The description adds essential constraints not present in the input schema: batch_ids must be between 2 and 5 values, and dimension provides an explicit enumeration (grain, hops, water, fermentation, measurements). 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 clearly states the tool's function with a specific verb ('Line up'), resource ('2-5 batches'), and scope ('on one dimension'). It is easily distinguished from sibling tools like get_batch (single batch) and list_batches (listing).
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 clear context for when to use the tool by describing the use case of comparing recipe versions and highlighting that 'absence is usually the thing you are looking for.' It does not explicitly name alternatives, but the purpose is unambiguous and the context signals suffice for correct selection.
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?
Annotations already declare read-only, idempotent, non-destructive behavior. The description adds valuable behavioral nuances beyond these flags: it explains why ordering is by brew date (Brewfather's _id ordering is not chronological) and discloses the compact row format, directing to get_batch for full records.
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 an intro, ordering rationale, Args list, and Returns section. Each sentence earns its place, and the rationale for ordering is genuinely useful rather than filler. It is compact and easily scannable.
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 purpose, parameters, return values, and relationship to get_batch. With an output schema present, the compact row summary is sufficient. The tool is simple, and the description fully addresses all necessary aspects for selection and invocation.
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%, so the description must compensate, and it does excellently. Every parameter (status, limit, newest_first) is explained with its meaning, allowed values, and default behavior. The ordering nuance adds semantic depth to newest_first that the schema cannot convey.
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 phrase 'List brew batches, newest first by default' explicitly names the action and resource. It distinguishes from sibling tools like list_recipes (recipes vs batches) and get_batch (compact vs full record), 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 includes 'Use get_batch for the full record,' an explicit pointer to a sibling alternative. It also explains the ordering rationale, helping the agent understand when this list tool is appropriate. It doesn't exclude other sibling tools like list_recipes, but the resource type (brew batches) makes the context clear enough.
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?
Annotations already mark this as read-only and idempotent. The description adds non-redundant behavioral details: minimal API call, no credential echo, and the exact return items (status, unit system, cache TTL, cache hit statistics). This goes beyond the annotations without contradicting them.
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: three short sentences with a bullet list for return values. It is front-loaded with the purpose and every sentence earns its place, providing function, behavior, and output without fluff.
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 is simple with no parameters, and the description covers purpose, safety, and return values. The presence of an output schema and comprehensive annotations means the agent has everything needed. The description is complete for the tool's complexity.
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 tool has zero parameters, so schema coverage is 100% by default. The baseline for 0 params is 4, and the description appropriately omits parameter details. It does provide return-value context, but that is not part of parameter semantics.
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: 'Verify credentials and report server configuration.' It is a specific verb+resource pair that distinguishes it from batch/recipe/inventory siblings. The added context about making one minimal API call and never echoing credentials reinforces its unique 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 use for credential verification and server configuration retrieval. 'Makes one minimal API call' signals low cost, and 'Never echoes credential values' reassures safety. However, it does not explicitly mention when not to use or name alternatives, but the sibling tools are unrelated enough that this is a minor gap.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds valuable behavioral detail: the exact formula, fallback logic for missing potential, the impact of volume_basis on reading, and the grain_only filter. This disclosure goes well beyond what annotations provide.
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: summary, formula, parameter explanations, and return values. Every section is purposeful and concise, with no redundancy or filler. The length is justified by the need to explain calculation nuances.
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?
Even with an output schema present, the description clearly lists the computed fields and aggregates, and explains all parameters and edge cases. It provides a complete picture for an AI agent to invoke the tool correctly and interpret results, making it fully contextual.
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 every parameter in depth: limit, grain_only, and volume_basis with defaults and detailed implications. This fully compensates for the absent schema descriptions and adds meaning that the schema alone would not convey.
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 'Compute brewhouse efficiency across recent batches' – a specific verb (compute) and resource (brewhouse efficiency) with clear scope. It distinguishes from sibling tools by focusing on aggregated efficiency analysis rather than listing or retrieving individual batches.
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 provides clear context on when to use (analyzing efficiency across batches) and explains parameter semantics like volume_basis and grain_only. However, it does not explicitly name alternatives or state when not to use it, so it's a clear context but missing 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?
Annotations already mark readOnlyHint and idempotentHint, but the description adds crucial behavioral details: the series is downsampled evenly, first and last readings are always preserved, and the summary is computed over the full series even when downsampled. This goes well beyond the annotations.
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-organized with a brief purpose, a concise behavioral note, and clearly labeled Args and Returns sections. It is appropriately sized for the tool's complexity, with every sentence contributing meaningful information.
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 description covers downsampling semantics, parameter effects, and the full return summary fields. It is self-sufficient for an agent to correctly invoke the tool, even without an explicit output schema present.
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's Args section fully explains each parameter: batch_id as the batch _id, latest_only as returning the most recent reading, and downsample_to with the meaning of 0 (disable downsampling). This compensates completely for the schema's lack of 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 opens with 'Fetch fermentation readings (Tilt or manual) for a batch,' which uses a specific verb and resource, clearly distinguishing it from sibling tools like get_batch or list_recipes.
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?
While the description does not name alternative tools, it provides clear context for when to use this tool (active ferment with thousands of points) and explains the downsampling trade-off. It lacks explicit exclusions or 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the operation as read-only, idempotent, and non-destructive. The description adds concrete behavioral details: it returns only user-supplied inventory, converts units per ingredient type, and includes spec figures, which exceeds what annotations convey.
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?
Three tight sections: purpose, args, returns. No fluff; every sentence carries operational meaning. The structure is conventional and easy to parse.
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 two-parameter list tool, the description covers execution constraints, input domain, and output format (units and spec figures). The existence of an output schema further reduces the need for return-type detail.
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 has no descriptions (0% coverage), so the description provides complete parameter semantics: category enumerates four valid values, and in_stock_only explains its filtering behavior and default. This fully compensates for the empty 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 a clear verb+resource: 'List inventory for one ingredient category.' It further clarifies scope by noting it excludes the full Brewfather database, distinguishing it from the sibling list_batches and list_recipes 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?
It states the tool is for listing user-added inventory only, with a caveat about not being the full database. However, it does not name an alternative tool for full database queries, so usage guidance is clear but not explicitly differentiated beyond sibling names.
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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