Blueprint MCP

Server Quality Checklist

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Latest release: v0.1.4
Disambiguation5/5
Each tool targets a distinct operation: compose finalizes output, group handles semantic grouping, group.update applies post-refresh decisions, refresh updates from filesystem, scan builds inventory, and task_context retrieves context. No two tools overlap in purpose.
Naming Consistency5/5
All tools follow a consistent 'blueprint.<verb>' or 'blueprint.<verb>.<subverb>' pattern (e.g., blueprint.compose, blueprint.group.update). The naming is predictable and hierarchical.
Tool Count5/5
With 6 tools covering scanning, grouping, refreshing, composing, and context retrieval, the count is well-scoped for the domain of Blueprint artifact management. Each tool earns its place.
Completeness4/5
The set covers the core lifecycle: scan, group, update groups, refresh, compose final output, and retrieve task context. A minor gap is the lack of a tool to explicitly delete or list groups, but the workflow seems complete for typical use.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on August 4, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
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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?
No annotations are provided, so the description must fully disclose behaviors. It fails to mention whether the tool is read-only, what side effects exist, or any authorization needs. The description is too sparse to inform the agent about behavioral traits.
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 a single concise sentence, front-loaded with the key action. However, the phrase 'Public tool' is slightly redundant and could be integrated better.
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?
With no output schema and 4 parameters, the description is too minimal. It does not explain what the 'analysis artifact' contains, nor does it address performance implications of maxFiles or ignore patterns.
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%, so baseline is 3. The description adds no parameter-specific information beyond what the schema already provides, which is acceptable given full schema coverage.
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 builds a file inventory, analyzes parseable code, and returns an analysis artifact. It implies a scanning purpose, but it does not explicitly differentiate from sibling tools like blueprint.refresh or blueprint.compose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The phrase 'for grouping' hints at a use case, but it does not provide clear when-to-use or when-not-to-use criteria.
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, and the description does not disclose behavioral traits like idempotency, side effects, authorization needs, or determinism beyond the term 'deterministic' which is vague. The description adds minimal value beyond the input schema.
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 sentence with no wasted words, efficiently conveying the core purpose.
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?
With no output schema and 6 parameters, the description is too brief. It fails to explain the return value structure or behavior, leaving significant gaps in understanding for an AI agent.
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 all parameters. The description adds no additional meaning beyond the parameter names and defaults, resulting in a baseline score of 3.
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 ('Return') and resource ('compact deterministic context slice from a Blueprint output artifact'), clearly distinguishing this tool from sibling tools like 'blueprint.compose' which likely creates a blueprint.
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 instructions on when to use this tool versus alternatives such as 'blueprint.scan' or 'blueprint.group', nor does it mention prerequisites or 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?
With no annotations provided, the description carries the full burden. It discloses that prepare does not finalize grouping and apply is deterministic, but it omits details like side effects (e.g., whether apply modifies data), authentication needs, rate limits, or error behavior. This is basic but not comprehensive.
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 three sentences, front-loaded with the main purpose. It efficiently covers both modes and includes key guidelines. Minor redundancy exists between the first and third sentences regarding apply mode, but overall it is well-structured and not overly verbose.
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 two-mode complexity and absence of output schema, the description covers the basics but lacks details on return format (e.g., what the 'compact packet' contains) and prerequisites (e.g., need for a prior blueprint.scan). It provides adequate context for an LLM but leaves some 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 coverage is 100% (all parameters described), so baseline is 3. The description adds value by explaining that 'plan' is required for apply mode and by providing usage hints that relate to parameter usage (e.g., using glob patterns for file paths). 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 defines the two modes, 'prepare' and 'apply', with distinct purposes: prepare returns a compact packet for semantic grouping, apply consumes a GroupingPlan and assigns files. This verb-resource pairing is specific and distinguishes the tool from siblings, even without explicit comparison.
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 for prepare mode, instructing the LLM to treat folder names as hints and prefer glob patterns. It also notes that plan is required for apply mode. However, it does not contrast with sibling tools or specify when to use this tool versus alternatives, leaving room for improvement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully details behavioral traits: conditions for executing assistantNextSteps, sub-agent spawning for hydrate-group-docs, and the prohibition on asking the user unless impossible. This is highly transparent and actionable.
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 sentences, no fluff. Purpose is front-loaded, behavioral instructions follow logically. Every sentence adds necessary 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?
Given the simple parameter set (2 params, no nested objects, no output schema), the description covers the core function and provides comprehensive behavioral rules, making it sufficient for correct invocation.
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%, providing baseline info for both parameters. The description does not add new meaning beyond what the schema already states about groupingArtifactId and language.
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 first sentence clearly states the verb 'Compose' and the resource 'frontend-ready Blueprint JSON' from a grouping artifact, defining a distinct purpose that differentiates from sibling tools like blueprint.group, blueprint.scan, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description only provides internal behavioral instructions (e.g., how to handle assistantNextSteps and hydrate-group-docs), not tool selection criteria.
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 provided, the description carries full behavioral disclosure. It describes side effects: writing blueprint files and scan state, and returning a maintenance prompt. It mentions comparison but does not explicitly state idempotence or whether it overwrites existing files, though 'refresh' implies that. Overall, it is transparent enough.
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 with three sentences, front-loading the core action. It is efficient but slightly dense; no unnecessary words. Could benefit from clearer structure (e.g., bullet points) but still scores high on conciseness.
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 6 parameters with full schema coverage and no output schema, the description covers the tool's purpose, process, return value (maintenance prompt), and follow-up guidance. It lacks error or prerequisite info but is sufficiently complete for an agent to use correctly.
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%, so baseline is 3. The description does not add specific parameter details beyond the schema but provides process context (e.g., 'compares .blueprint/refresh-scan.json with a fresh full scan'). This is adequate but not extra value.
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: 'Deterministically refresh .blueprint/blueprint-output.json from the current filesystem snapshot.' It explains the process (comparison and writing) and distinguishes itself from siblings by specifying a follow-up tool and what it does not do (no raw git diffs).
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 advises when to use blueprint.group.update afterwards and for what conditions ('only for unassigned files or empty group decisions'). It implies usage context by stating the tool is deterministic and does not send raw diffs, but it lacks explicit alternatives 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the tool writes .blueprint/blueprint-output.json and creates group markdown templates, and implies state mutation. However, it does not mention error handling, side effects on existing groups, or backup behavior. With no annotations, the description carries the full burden and does fairly well.
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?
Reasonably concise given the complexity; front-loads purpose, then usage guidelines, then validation rules. Some redundancy (e.g., 'Do not use this tool for...' could be shortened) but overall well-structured.
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?
Covers the tool's output (files written) and validation rules, sufficient for a mutation tool. Lacks mention of return value or error states, but the core behavior is well described. With no output schema, the description does its job.
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?
Adds significant meaning beyond the input schema by detailing validation rules for each field in the nested decision object (e.g., fileId must refer to unassigned, newGroups id must not exist). This compensates for the 50% schema coverage and makes parameters self-documenting.
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 applies LLM group decisions after a Blueprint refresh, specifying three allowed actions: assign unassigned files to existing groups, create new groups, and delete empty groups. It distinguishes from siblings by listing what not to use it for.
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?
Explicitly states when to use (only for the three actions) and when not to use (e.g., updated files, deleted file cleanup, editing markdown). Validation rules provide clear constraints, making it easy for an agent to decide correct invocation.
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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