byaml-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct concern: schema retrieval, graph construction from three different input sources, graph validation, downstream impact analysis, cost aggregation, and remediation planning. No two tools overlap in purpose, making selection unambiguous.
Naming Consistency4/5
Names follow snake_case and mostly verb_noun (validate_graph, cost_summary, remediation_plan), with graph_from_* indicating source. Minor deviations like schema_latest and blast_radius are still clear and consistent in style, so no confusion arises.
Tool Count5/5
With 8 tools, the surface is well-scoped for an architecture analysis server. Each tool contributes to a clear workflow (create graph, validate, analyze, remediate), and the count is within the ideal range for coherence.
Completeness4/5
The set covers graph generation from multiple sources, validation, blast radius, cost, and remediation. Minor gaps exist (e.g., no graph diff or editing), but the core lifecycle for analysis is complete and no dead ends are apparent.
Average 3.4/5 across 8 of 8 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under Apache 2.0.
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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

Behavior2/5
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 states that the tool returns errors/warnings, which implies a read-only operation, but does not explicitly confirm it has no side effects. It also doesn't disclose any rate limits, authentication requirements, or what happens to the input (e.g., whether it mutates the graph). The description is too thin to set expectations.
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 single, compact sentence that immediately states the action and expected output. There is no filler or redundant content. It is front-loaded with the core purpose and ends with a reference to the validation engine, maintaining efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is incomplete for an agent that needs to call this tool correctly. It lacks details about the return format of errors/warnings, the validation criteria, whether the graph is mutated, and how to handle the absence of an output schema. The sibling tool schema_latest suggests a schema source exists, but the description does not reference it or explain how the graph version relates to that schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It identifies the sole parameter 'graph' as an 'Architecture Graph v0.4', giving some semantic context, but it doesn't explain required fields, valid structure, or how errors relate to the graph's properties. The schema is permissive (additionalProperties: true), so the description should have provided more detail but does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb (validate), a resource (Architecture Graph v0.4), and an output (errors/warnings). It clearly distinguishes from sibling tools like graph_from_evaluation or blast_radius, which generate or analyze graphs. The reference to 'byaml.validate' adds minor ambiguity but doesn't obscure the core purpose.
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 guidance on when to use this tool versus alternatives. It doesn't mention when validation is appropriate, whether it should be run before other tools, or if there are conditions that make it necessary. The only implicit hint is that it validates a specific graph version, but there is no explicit 'use this when...' context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states the tool outputs a cost summary and how to group, but it does not disclose whether it is read-only, whether it performs heavy computation, how it handles incomplete graphs, or what the output structure is. For a tool that likely reads data, this leaves safety and side-effect information missing.
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 concise and to the point: two lines of purpose and a short list of arguments. It front-loads the purpose and then explains the 'by' values. No filler or redundancy. It could be slightly structured better (e.g., markdown formatting) but is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a nested object parameter 'graph' with zero schema description coverage, no output schema, and no annotations. The description does not explain what the output looks like, whether it's a single number or a breakdown, nor does it mention versioning requirements beyond 'graph JSON v0.4'. The description is insufficient for an agent to confidently call the tool with a properly structured graph and interpret results reliably.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the description is the only place to explain parameters. It explains 'by' (agrupar por 'layer' | 'type' | 'owner') but does not explain 'graph' beyond 'graph JSON v0.4'. The 'by' explanation adds value beyond the schema, but the 'graph' parameter is left vague: no indication of required structure beyond the version tag, and the schema marks it as a generic object with additionalProperties.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Resumen de costo del grafo' (cost summary of the graph). It identifies the tool as computing a summary of costs from a graph, and lists valid grouping values for the 'by' parameter. However, it does not explicitly distinguish it from siblings beyond the obvious 'graph' input, though the purpose is clear enough.
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 when to use the tool: when you need a cost summary of a graph, grouping by the specified dimensions. It does not provide explicit when-not-to-use guidance or mention alternative tools like blast_radius or remediation_plan, which are functionality-distinct. The absence of explicit alternatives slightly reduces the score, but the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 the conversion action but does not disclose any side effects, prerequisites (e.g., terraform CLI availability), error behavior, or output format details beyond the version. For a tool that processes a file, it lacks information about what happens on invalid input or whether it modifies anything.
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 concise and front-loaded with the core purpose. The Args section is structured and efficient, though the account description could be slightly more informative. No wasted 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 tool's moderate complexity (2 params, no output schema), the description covers the main input and output but lacks details on error handling, output structure, and the exact role of the account parameter. It is adequate for a basic call but not fully complete for an agent that might need to handle edge cases.
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 0%, so the description must compensate. It explains plan_path with a concrete example command, which adds value beyond the schema's bare 'Plan Path'. However, the 'account' parameter is only described as 'opcional, cuenta/entorno' with no further detail on its format or effect, leaving a gap for that parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool converts a `terraform show -json` plan into an Architecture Graph v0.4, specifying the input format and the output. It distinguishes itself from siblings like graph_from_account and graph_from_evaluation by the input source (Terraform plan), though it doesn't explicitly name them.
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 specifying the required input (plan_path) and the command to generate it, but it does not explicitly state when to use this tool versus alternatives like graph_from_account or graph_from_evaluation. The context is clear for the primary use case, but no exclusions or alternative routing are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output (IaC steps) but says nothing about whether the tool is read-only, its performance, or how the `graph` parameter influences execution. It leaves open whether invoking this tool has side effects, which is notable for a plan generator.
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 follows a compact docstring format, leading with a one-line purpose followed by structured Args. It stays short but loses a point for mixing Spanish and English and not restating the `graph` default that appears in the schema, which could confuse non-bilingual agents.
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?
For a 2-parameter tool with no annotations or output schema, the description covers the essentials. However, it omits the exact structure of the returned plan (beyond 'pasos IaC'), whether an empty findings list returns an empty plan, and whether there's a limit on findings size. These are meaningful gaps given the bare schema and absent 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?
Despite 0% schema coverage and untyped properties in JSON Schema, the description fully documents both parameters: `findings` gets a clear JSON example with structure hints, and `graph` is marked optional with a version note. This precisely compensates for the lack of schema descriptions, making the tool callable incorrectly impossible for a careful agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Genera un plan de remediación') and the resource ('findings'), with a concrete output (IaC steps with Terraform snippets). It doesn't explicitly differentiate from siblings, but the tool name and description make its role unambiguous next to graph/schema-oriented siblings.
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 explains what the tool does but gives no guidance on when to use it versus alternatives like `blast_radius` or `validate_graph`. There are no preconditions, exclusions, or hints about when the optional `graph` parameter should be provided, so agents must infer usage from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It does disclose that it fetches from schema.byaml.org and returns specific data, implying a network operation. However, it does not explicitly state whether it is read-only, any failure modes, or authentication requirements. For a simple fetch, this is minimal but not contradictory.
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 two lines, front-loading the purpose and source. It is efficient and avoids fluff. However, it could be slightly more structured by including the response format or emphasizing the read-only nature, but it is appropriately sized for a zero-parameter tool.
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 tool with no parameters and no output schema, the description provides the essential information: what it fetches and what it returns. It does not mention error handling, response format, or prerequisites, but given its simplicity, these are not critical gaps. Adequate for an agent to call it successfully.
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 the description does not need to clarify parameter meaning. The baseline for zero parameters is 4, and the description does not detract from that.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches the current schema version and a catalog summary, specifying the external source (schema.byaml.org) and the output (latest + count of types for catalog v0.4). This is a specific verb+resource and distinguishes itself from sibling tools that focus on graph, cost, or blast radius analysis.
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?
There is no guidance on when to use this tool versus alternatives. It merely states what it does without providing context on appropriate scenarios, prerequisites, or cases where it should not be used. Given zero parameters, this could be acceptable, but the description offers no routing clues.
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 the full behavioral burden. It discloses the two operating modes and the fact that sample: true avoids needing AWS credentials — this is genuinely useful behavioral info. But it omits the output shape, any side effects, and doesn't explain what a 'scan' (SOFE) is, so it doesn't fully compensate for the absence of annotations.
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 main sentence is a clean single statement of function, and the additional note is directly relevant to parameter use. There's one unexplained acronym (SOFE) that could cause confusion, but overall the text is tight and well-structured.
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 description covers the key operational detail (sample mode vs. real scan) but leaves gaps: the 'profile' parameter is not explained, 'SOFE' is undefined, and there is no mention of output shape or error conditions. With no annotations at all, these omissions matter for an agent deciding how to call this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0/0% — with no descriptions in the schema, the description's job is critical. It explains the sample parameter's behavior well (true = example graph, false = expect prior scan), but the profile parameter is never described. An agent cannot tell what 'profile' means beyond its name, which is a substantial gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Convierte un perfil AWS (scan) en un Architecture Graph v0.4' (converts an AWS profile scan into an Architecture Graph). This clearly distinguishes it from sibling tools like graph_from_terraform or graph_from_evaluation, which use different input sources. The output format and version are also specified.
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 gives clear usage context for the sample parameter ('con sample: true devuelve un grafo de ejemplo' without requiring AWS credentials, versus expecting a prior scan with sample: false). However, it gives no guidance on when to use this tool instead of its siblings (graph_from_terraform, graph_from_evaluation), which is the main vertical comparison the agent needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so this dimension carries full weight. The description explains the concept (downstream impact) but doesn't disclose specifics like whether it mutates state (it's a read/compute, likely safe), performance implications (deep graphs or infinite depth), or error handling. It mentions max_depth=None means infinite, which is a useful behavioral note. However, it doesn't state whether it's read-only or if there are 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise: a one-line summary plus an args list. It's front-loaded with the concept, then details params. Every sentence adds information. Minor redundancy: 'Downstream impact de un nodo' uses Spanish while the rest is English, which could be cleaner, but it's not verbose. The structure is clear and skimmable.
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 it's a compute tool with 4 params, no output schema, and no annotations, the description covers the key behaviors: what it does, how to use it, and what the args mean. It lacks details on return format (downstream impact as what data structure?), but the description explains the concept enough for an agent. It could be improved by explaining what the output looks like, but it's reasonably 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 0%, so the description must compensate. It does explain each parameter in the docstring: node_id (id of origin node, example given), graph (graph JSON v0.4, output of graph_from_*), max_depth (max depth, None = infinite), rel_types (filter to these relType). This is solid, but it doesn't specify the format of graph beyond 'v0.4' or give example values for rel_types beyond 'invokes'. Minimal compensation for zero schema 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 purpose: compute downstream impact of a node (what breaks if it fails). It uses a specific verb ('compute'), resource ('node in a blast radius graph'), and gives a concrete example. It clearly distinguishes from siblings like graph_from_* (which build graphs) and validate_graph (which validates), so an agent can tell when to use this versus others.
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 by showing the graph is an output of graph_from_* functions, suggesting a workflow. It doesn't explicitly state when not to use it or name alternatives, but the arg examples ('node_id' like 'api', 'rel_types' like 'invokes') give clear context on how to call it. It could be improved by mentioning it relates to blast radius analysis only, but the context is mostly clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full transparency burden. It explains that the tool builds an Architecture Graph from findings, which is the core behavior, but it does not state whether the graph is returned, persisted, or whether the operation affects existing state.
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 compact one-line purpose statement followed by a clean Args list. Every sentence adds practical 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The inputs are fully covered and the output type is named, which is enough to call the tool correctly. However, because there is no output schema, the description could be even more complete by explicitly stating what the tool returns or whether it has side effects.
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 documents all four parameters meaningfully: findings include resource_id/resource_type/severity/policy, cost_map maps resource_id to monthly_cost, eval_id is the evaluation ID, and account is shown with an example. This fully compensates for the sparse input 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 uses a concrete verb ('Construye') and identifies a specific output ('Architecture Graph v0.4') plus the exact input source ('evaluación SOFE/findings'). This makes it easy to distinguish from sibling tools like graph_from_terraform or graph_from_account.
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 phrase 'desde una evaluación SOFE' provides a clear usage context: this tool should be used when the agent has SOFE findings to convert into a graph. It does not explicitly say when to avoid it in favor of graph_from_terraform or graph_from_account, so it lacks an explicit when-not rule.
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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