mcp-json-yaml-toml

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools are distinct, but 'data' and 'data_query' both support reading data; agents may be unsure which to use for extraction queries. Descriptions help differentiate (data for CRUD, data_query for queries/transformations), but some overlap remains.
Naming Consistency4/5
The majority follow a consistent 'data_<verb>' pattern (convert, diff, merge, query, schema) and 'constraint_<verb>' pattern. However, the bare 'data' tool without a suffix breaks the pattern, creating a minor inconsistency.
Tool Count5/5
With 8 tools covering CRUD, format conversion, diff, merge, query, schema, and constraints, the count is well-scoped for the domain. Each tool serves a clear purpose without unnecessary bloat or undercoverage.
Completeness5/5
The tool surface provides full lifecycle support for JSON/YAML/TOML files: read, write, delete, format conversion, comparison, merging, querying, and schema operations. No obvious gaps for common use cases.
Average 4.5/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- Last stable release on July 6, 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.
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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?
Annotations provide readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false, which convey basic safety profile. The description adds the fact that the tool performs schema operations but does not elaborate on behavioral details such as side effects of adding directories/catalogs, error handling, or permissions. It complements but does not significantly extend beyond 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-structured with a concise summary, a bullet list of actions, and clear examples. Every sentence is informative and earns its place. The front-loaded format allows quick comprehension of the tool's purpose and capabilities.
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 tool's complexity (7 actions, 12 parameters) and the presence of an output schema, the description sufficiently covers each action's purpose and provides illustrative examples. It could be more complete by explaining when to use each action (e.g., prerequisites for add_dir vs. add_catalog) but is not missing critical information for typical usage.
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?
Input schema covers all 12 parameters with descriptions (100% coverage), so baseline is 3. The description adds value by providing concrete examples showing parameter combinations for specific actions (e.g., 'action="validate", file_path="config.json"'), which helps understand typical usage beyond the schema's individual parameter 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 it is a 'Unified schema operations tool' and enumerates all seven specific actions (validate, scan, add_dir, add_catalog, associate, disassociate, list). Each action has a brief purpose, and the examples solidify understanding. The tool is well differentiated from siblings, which cover constraints, data manipulation, and queries rather than schema operations.
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 lists actions and provides examples, but does not explicitly state when to use this tool versus alternatives. Sibling tools are conceptually distinct (e.g., constraint_validate vs. schema validation), but the description offers no exclusions or context for choosing this tool. Usage is implied but not explicit.
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 indicate destructiveHint=true and readOnlyHint=false. The description adds valuable behavioral details: side effects ('Modifies file on disk if operation is 'set' or 'delete''), failure modes ('FileNotFoundError if file missing'), and output contract. 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 well-structured and concise: a one-line summary, usage guidance, then structured sections for output contract, side effects, failure modes, and operation bullets. Every sentence adds value without redundancy.
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 tool's complexity (10 parameters) and the presence of an output schema, the description covers output contract, side effects, failure modes, and operations. It is comprehensive, though it could briefly mention parameter interactions (e.g., required parameters per operation) to reach full completeness.
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?
The input schema has 100% description coverage, so the baseline is 3. The description does not add additional meaning beyond the schema; it only reiterates operation semantics. No parameter-specific clarification is provided that isn't already in 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's purpose: 'Get, set, or delete data in JSON, YAML, or TOML files.' It uses specific verbs and resource types, and the bullet list of operations further clarifies. The description distinguishes this tool from siblings like data_query and data_convert by focusing on basic file CRUD operations.
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 a direct usage statement: 'Use when you need to get, set, or delete specific values or entire sections in a structured data file.' This provides clear context. However, it does not explicitly exclude alternative tools or specify when not to use it, so it lacks full comparative 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?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the agent knows it's a safe read operation. The description adds return format details but no extra behavioral context beyond 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?
Short paragraph front-loads purpose and uses bullet-like structure for output keys. Every sentence is informative with no 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?
Given the tool's simplicity (no params, output schema detailed in description), the definition is complete. Annotations cover safety, and description covers return format. No gaps.
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?
Input schema has no parameters (100% coverage), so description cannot add parameter info. However, it thoroughly explains the output structure, which is helpful for the agent to understand what the tool returns.
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 returns a list of registered LMQL constraints with metadata, using specific verb+resource. It distinguishes from siblings like constraint_validate, which validates a value against a constraint.
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 usage for retrieving constraints with their metadata, but does not explicitly state when to use this tool vs alternatives like constraint_validate. However, the context is clear and no exclusions are needed.
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 provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: side effects (none), failure modes (ToolError for missing files or disabled formats), and output contract (DiffResponse with has_differences, differences, statistics, summary). No contradictions.
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 four sentences, each serving a purpose: purpose, details, output contract, side effects/failures. No redundant information.
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 presence of output schema and annotations, the description adequately covers purpose, supported formats, output structure, and failure modes. It could mention performance but is sufficient for typical use.
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 coverage is 100% with all three parameters described. The description adds overall context (e.g., cross-format comparison) but does not enhance individual parameter meanings beyond the schema. Baseline score of 3 is appropriate.
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 'compare' and the resource 'configuration files', and specifies the output 'structured differences'. It distinguishes from sibling tools like data_merge or data_convert by focusing on diffing.
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 when to use the tool: for deep comparison of configuration files in JSON, YAML, TOML, including cross-format. It does not explicitly mention when not to use it or provide alternatives among siblings, but the context is clear.
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?
Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds output contract format, confirms no side effects, and lists failure modes (FileNotFoundError, ToolError). 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 concise with three clear sections: purpose, usage, and contract/behavior. Every sentence adds value, no fluff. Front-loaded with primary purpose.
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 complexity (5 params, 2 required, output schema present), the description fully covers purpose, usage, output contract, side effects, and failure modes. No gaps remain.
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 coverage is 100% with detailed parameter descriptions. The description does not add significant meaning beyond the schema (e.g., mentions 'yq expression' and supported formats). Baseline 3 applies.
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 extracts, filters, or transforms data without modification, using a specific verb and resource. It distinguishes from siblings like data_convert or data_merge by emphasizing read-only querying on JSON, YAML, TOML files.
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 explicitly states when to use the tool (extracting/filtering/transforming without modification) and provides output contract and failure modes. It lacks explicit when-not-to-use or alternative tools, but the context is clear.
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 read-only, idempotent, non-destructive behavior. The description adds significant value: explicit side effects (none), failure modes (ToolError for unknown constraint), output contract (valid, error, is_partial), and a comprehensive list of available constraints with examples. No contradictions 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 concise and well-structured: purpose, usage guidance, output contract, side effects, failure modes, and constraint list. Every sentence is informative with no redundancy or unnecessary 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 tool's simple validation purpose, the description covers all necessary aspects: when to use, output structure, available constraints, side effects, and failure modes. Annotations safely cover behavioral traits, and the implied output schema complements the description. No gaps for an effective 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?
Schema coverage is 100% and its parameter descriptions are decent, but the description enriches the constraint_name parameter with a full list of constraints and examples (e.g., 'YQ_PATH: Valid yq path (e.g., ".users[0].name")'). This adds meaning beyond the schema's brief example list.
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 validates a value against an LMQL-style constraint, with a specific verb and resource. It distinguishes from siblings like constraint_list (which lists constraints) by focusing on validation. The context 'before using it in other operations' further clarifies purpose.
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 on when to use this tool: to check validity before other operations. It also mentions partial validation support. However, it does not explicitly state when not to use it or list alternatives, which would improve differentiation.
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?
The description fully discloses the deep merge behavior, override semantics, and output options (file vs. response). Annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) are consistent and complemented by the description's details on return values and errors.
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 a summary, detailed parameter list, return info, and error notes. It is concise yet comprehensive, using clear sections without unnecessary 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 tool's complexity (4 parameters, 2 required), full schema coverage, and presence of output schema, the description covers all necessary aspects: behavior, parameters, return values, and error conditions. No gaps identified.
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 meaning: it explains that file_path2 values override file_path1, clarifies default format behavior, and describes the conditional return structure. This adds value 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 merges two files into a deep-merged configuration. It uses specific verbs ('Merge') and resources ('two files', 'deep-merged configuration'), distinguishing it from sibling tools like data_diff or data_convert.
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 explains the general usage (merge two files with overlay semantics). It does not explicitly state when not to use it or list alternatives, but the sibling tool names provide context that this is for merging, not diffing or converting.
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?
The description discloses side effects ('Writes to output_file if provided'), output contract, and failure modes, adding context beyond annotations. It is consistent with idempotentHint and destructiveHint, though readOnlyHint is contradicted (see below).
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 4 sentences, front-loading the action ('Convert file format') and using structured sections for output contract, side effects, and failure modes, with 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?
The description covers the conversion use case, all parameters, side effects, failure modes, and output contract. Given the output schema exists, no further detail 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?
Schema coverage is 100%, and the description adds meaning by explaining the dual behavior (return string vs. write to file) based on output_file parameter, and implicitly confirms output_format via listed formats.
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 'Convert file format' and specifies the supported formats (JSON, YAML, TOML), clearly stating the tool's function. It is distinct from siblings like data_diff and data_merge, which do not perform format conversion.
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 explicitly states 'Use when you need to transform a file from one format (JSON, YAML, TOML) to another,' providing clear usage context. However, it lacks explicit when-not-to-use guidance or alternatives, though siblings do not compete.
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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