Blockbench MCP Bridge

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.4.0
Disambiguation5/5
Each tool has a clearly distinct purpose: add_animation, apply_model, audit_model, build_from_reference, capture_preview/turntable, create_pet/rig, export, get_project, list_capabilities, open, patch, quality_report, save, set_camera, status. There is no overlap that would cause confusion.
Naming Consistency5/5
All tools follow the consistent pattern 'blockbench_verb_noun' (e.g., blockbench_add_animation). No mixing of conventions.
Tool Count4/5
With 17 tools, it is slightly above the ideal 3-15 range, but the count is justified by the breadth of Blockbench functionality covered. It feels well-scoped rather than bloated.
Completeness4/5
The tool surface covers creation, modification, auditing, animation, export, project management, and visual inspection. Minor gaps like texture editing or undo are absent, but core workflows are fully supported.
Average 3.1/5 across 17 of 17 tools scored. Lowest: 2.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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, the description must disclose behavior. It hints at codec-driven export but does not mention side effects (e.g., file creation, overwriting), required permissions, or what determines the 'active' codec. Too brief to inform safe usage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but lacks structure (no separation of purpose, usage, or details). It's appropriately short but not well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters, no output schema, and no annotations, the description is severely incomplete. It fails to explain the return value, codec options, path requirements, or any behavioral details, leaving the agent with insufficient context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% for both parameters. The description only mentions 'codec' with examples but does not explain the required 'path' parameter (format, purpose). It adds minimal meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it exports using a codec and gives examples (Minecraft Java item JSON, .bbmodel). However, 'active format codec' is vague and doesn't clearly define the tool's primary action. It distinguishes from siblings like save or capture, but could be more explicit.
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 guidance on when to use this tool vs alternatives like blockbench_save_project or blockbench_capture_preview. The description does not mention prerequisites, limitations, or preferred scenarios.
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?
With no annotations provided, the description must disclose behavioral traits. It only states 'set' without indicating side effects, state changes, or constraints. For example, it does not mention whether the change is persistent, affects subsequent captures, or requires specific permissions. This is insufficient for a tool with 4 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, achieving conciseness. However, it is overly terse, sacrificing informative content. It could be expanded without becoming verbose. The structure is front-loaded but lacks necessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, and no annotations, the description is severely incomplete. It fails to specify parameter effects, defaults, or return behavior. An AI agent would lack essential context to correctly invoke the tool, especially for the required 'position' parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must explain parameters. It provides none. The four parameters (position, fov, target, orthographic) are not mentioned, leaving the agent to guess their meaning and usage from names alone. This is a critical gap.
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's action ('Set the active Blockbench preview camera') and its intended context ('before visual inspection'). It effectively conveys the primary purpose. However, it could be more specific about what 'preview camera' entails, especially distinguishing it from other camera-related operations in siblings like blockbench_capture_preview.
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. While it implies use before visual inspection, it does not explicitly say when not to use it or mention any prerequisites or alternatives among the siblings. This lack of usage context leaves the agent to infer appropriateness.
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 must compensate. It states 'capture' but does not disclose whether it saves an image, returns a URL, or affects the viewport. No mention of permissions or 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that clearly states the action and purpose, but it is too brief to cover necessary details for an agent.
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 3 parameters and no output schema, the description lacks critical context about parameter effects, return value, and limitations, making it incomplete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% (no parameter descriptions in schema). The description does not explain the three parameters (crop, width, height), leaving their purpose and constraints unclear.
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 captures the active 3D viewport for visual verification of proportions, silhouette, and colors. However, it does not differentiate from the sibling tool 'blockbench_capture_turntable', which also captures the viewport.
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 use for visual verification but provides no guidance on when to use this tool versus alternatives like 'blockbench_capture_turntable' or other viewport tools.
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 full burden. It mentions 'atomically' but does not explain what atomicity entails (e.g., rollback on failure). No disclosure of destructive behavior (mode 'replace' overwrites) or error handling, rate limits, or state changes.
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 sentence that conveys the primary function without redundancy. While it is dense, it remains brief and front-loaded. However, it could be slightly split for readability.
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?
Given the tool's complexity (7 params, deeply nested objects, no output schema, no annotations), the description is insufficient. It does not explain mode semantics, atomicity guarantees, how to structure input for common tasks, or what happens on conflict. The agent lacks context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description adds no parameter-level explanations. It lists component types but does not clarify how 'mode' works, the relationship between 'cubes' and 'groups', or which fields are mandatory beyond the schema. The agent must infer everything from the schema alone.
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 creates or appends a Blockbench model from specific components like groups, cubes, UVs, textures, etc. It uses a specific verb ('apply model') and resource, and distinguishes from siblings such as blockbench_patch_model and blockbench_create_rig.
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 guidance on when to use this tool versus alternatives like blockbench_patch_model for partial updates or blockbench_create_rig for skeleton creation. No mention of prerequisites or decision criteria for 'replace' vs 'append' mode.
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 bears full responsibility. It lists what is checked but does not disclose whether the tool is read-only, whether it modifies the model, or what happens on failure. The behavioral implications are insufficiently clarified.
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 sentence listing 8 items, which is relatively concise and front-loaded with the verb 'Audit'. However, the long list with 'and' could be more structured, e.g., using bullet points, but it remains clear and 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?
Given the complexity of the audit (many checks) and the lack of an output schema, the description is incomplete. It does not explain the output format, return values, or error handling, leaving the agent with insufficient information about what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention the 'profile' parameter at all. It fails to explain the meaning or impact of the enum options (generic, weapon, pet, entity), leaving the agent without guidance on how to use the parameter.
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: auditing the active model for specific issues like invalid bounds, missing textures, etc. It uses a strong verb 'Audit' and lists concrete checks, distinguishing it from sibling tools like blockbench_quality_report.
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 guidance on when to use this tool versus alternatives, no prerequisites or context for usage. The description fails to indicate when auditing is appropriate or when other tools should be preferred.
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 must disclose behavior fully. It mentions atomic refinement and use of stable identifiers, but does not explain side effects (e.g., whether changes are permanent), required permissions, or what happens to removed cubes. The description is too vague to fully inform an agent about the tool's behavior.
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 sentences and front-loaded, efficiently stating the purpose and usage context. It could be slightly more structured, but it is concise without wasted words.
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?
Given the complexity (3 parameters, no output schema, no annotations), the description is too brief. It does not mention required fields, parameter purposes, or return values. For a tool with zero annotations and schema coverage, more detail is needed to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description provides no explanation of the parameters (cubes, groups, remove_cubes). It does not mention that cubes require an 'id' field or describe the structure of the arrays. The description adds no value beyond the schema for parameter understanding.
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 refines or removes cubes/bones by stable name/UUID after visual review, and specifies it is for turntable-driven correction without rebuilding. However, it does not explicitly distinguish from siblings like blockbench_apply_model or blockbench_audit_model, leaving some ambiguity.
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 provides a clear usage context ('for turntable-driven correction without rebuilding') but lacks explicit guidance on when not to use the tool or alternatives. It implies that rebuilding from scratch is a different use case but does not name specific sibling tools.
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?
Minimal disclosure. Does not mention overwrite behavior, error handling, or required state (e.g., unsaved changes). With no annotations, the description should provide these details.
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?
Single sentence with no wasted words, but could include more relevant detail without becoming verbose.
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?
Lacks output schema, no param descriptions, and insufficient behavioral context for a save operation. Does not address common concerns like file overwrite or success indication.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description adds no explanation for the 'path' parameter. Agent cannot infer whether it expects full path, filename only, or extension requirements.
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?
Clear verb 'Save', specific resource 'active project', and output format '.bbmodel file' with location 'on the local machine'. Differentiates from siblings like export_model or open_project.
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 guidance on when to use this tool versus alternatives like export_model or capture_preview. No prerequisites or context provided.
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 burden. It states the tool adds an animation, which is a mutation, but doesn't disclose side effects, prerequisites (e.g., must have a project open), or behavior on error. Adequate but not detailed.
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?
A single sentence that efficiently captures the tool's purpose without extraneous words. Front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the `animation` object (nested fields, many options), the description is too high-level. No output schema exists, and annotations are missing, leaving gaps about expected results and error handling.
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%, yet the description only lists general animation features and does not explain the structure of the `animation` parameter or its nested fields. The description adds minimal value beyond what the schema already shows.
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 identifies the verb 'Add' and resource 'one advanced animation to the current project', listing specific features. It distinguishes from sibling tools by its focus on animation, but could be slightly clearer about the 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?
No guidance on when to use this tool vs alternatives. Sibling tools include various model operations but no other animation tools, so usage context is implied but not explicitly stated.
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 provided, so description carries full burden. It mentions 'automatically frame and capture' but does not disclose side effects (e.g., modifies scene, returns images, destructive actions). Missing behavioral context like whether it overwrites previous captures or requires specific project state.
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?
Single sentence is concise and front-loaded with key purpose. However, structure could be improved to separate details like parameter hints or behavioral notes. Every word earns its place.
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 5 parameters, no output schema, and no annotations, the description is incomplete. It does not mention output format (e.g., captured images), prerequisites (active model), or limitations. Agent likely needs additional context to use correctly.
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 low (20%) with only 'distance' having a description. The description lists view names matching the enum but does not explain other parameters (width, height, orthographic) or their defaults/ranges meaningfully beyond schema basics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states action (frame and capture) and resource (active model) and specifies the views (hero, front, side, back) and context (AI visual refinement loop). It distinguishes from siblings like blockbench_capture_preview which likely captures a single view.
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?
Description implies usage in AI refinement loop but does not explicitly state when to use versus alternatives, nor provide when-not-to-use or exclusions. Compared to siblings like blockbench_capture_preview or blockbench_set_camera, guidance is minimal.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the tool is read-only, requires an open project, or any side effects. The description only lists what it returns without behavioral context.
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 that efficiently conveys the tool's function without unnecessary words. It is front-loaded and earns its place.
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 no parameters and no output schema, the description is minimal. It does not explain what 'capabilities' or 'codec' entails, nor provide context on how the output should be used. Slightly below adequate for a simple list tool.
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, and the schema coverage is 100%. The description does not need to add parameter semantics. Baseline of 4 is appropriate.
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 that the tool lists model formats and the capabilities/codec of the active format. The verb 'list' and resource 'model formats' are specific, and it distinguishes from sibling tools that perform modifications or exports.
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, such as before exporting or selecting a format. There is no mention of 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?
Without annotations, the description discloses that the tool creates a rig with specific components but omits details on side effects (e.g., whether it modifies an existing project) or prerequisites (e.g., an open project). It adds some value beyond a simple label.
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, front-loaded sentence that efficiently conveys the tool's purpose and included features, though it could be slightly more structured with bullet points for clarity.
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 6 parameters, no output schema, and no annotations, the description provides a reasonable overview but lacks details on return values, project state changes, and parameter constraints beyond the profile enum.
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?
Only one parameter (format) has a schema description. The description lists profile types but does not explain required parameters like name, scale, or texture dimensions. With 6 parameters and low schema coverage, the description fails to add sufficient meaning.
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 creates a 'professional empty rig preset' and lists specific categories (weapon, pet, golem) and included elements (VFX bones, hitbox, attachment locators), distinguishing it from siblings like blockbench_create_pet.
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 states what it does, without mentioning when not to use it or providing context for selecting it over siblings like blockbench_build_from_reference.
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 burden. It indicates a non-destructive read ('Read a compact snapshot') and lists returned elements, but does not detail limitations, permission requirements, or whether repeated calls produce consistent results. The 'compact' qualifier suggests a subset, which is helpful but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core action and lists contents. No wasted words; every clause serves a 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?
Given the tool has an unexplained parameter, no output schema, and no annotations, the description is incomplete. It omits how the parameter affects output and the exact structure of the snapshot. For a read operation, details about pagination or format are missing, leaving the agent to infer from tool name only.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has one parameter 'include_uv' but the description provides zero explanation of what it does. With 0% schema description coverage, the description fails to add any meaning beyond the schema's default value. The AI agent has no guidance on when to set it to true.
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 'Read' and the resource 'compact snapshot of the open Blockbench project', listing specific contents (groups, cubes, textures, animations, bounds). This distinguishes it from sibling tools like blockbench_open_project or blockbench_save_project, which have different purposes.
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 this tool is for reading project state, but provides no explicit guidance on when to use it versus other read-like tools (e.g., blockbench_status). No exclusions or alternatives are mentioned.
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 bears full responsibility for behavioral disclosure. It describes the tool as scoring quality, which suggests a read-only operation, but does not explicitly state that the model is not modified, nor does it mention any permissions, side effects, or other behavioral traits beyond the action itself.
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, efficient sentence that front-loads the action and lists key criteria. Every word adds value; no redundancy or filler.
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 no output schema, so the description should clarify the return format (e.g., numeric score, structured report). It states 'score the active model's art quality' but does not describe the output structure, leaving ambiguity. With one parameter and low complexity, this gap is notable.
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 schema has one parameter (profile) with an enum of generic, pet, weapon, golem. The description mentions 'professional weapon, pet, and golem packs' but does not explain the impact of each profile or how to choose. With 0% schema description coverage, the description partially compensates by hinting at the profiles but lacks explicit guidance.
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 scores the active model's art quality, listing specific evaluation criteria (silhouette layering, semantic naming, etc.) and referencing professional packs. This verb+resource combination distinguishes it from sibling tools like blockbench_audit_model or blockbench_create_rig.
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 the tool is used for evaluating art quality against professional standards but does not explicitly state when to use it versus alternatives (e.g., blockbench_audit_model) or when not to use it. Usage context is only implied by the listed 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, so the description carries full burden. It states the action ('open') and the result ('new tab for inspection/editing'), but omits side effects, error conditions, permissions, or behavior if the file is already open. This is insufficient for safe invocation.
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?
A single, front-loaded sentence with no unnecessary words. Every part adds value: verb, resource type, source, and purpose.
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 simple tool with one parameter and no output schema, the description covers the core purpose and parameter context. However, it fails to mention return behavior (e.g., success/error indication) or error handling (e.g., file not found), leaving gaps.
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?
With only one parameter (path) and 0% schema description coverage, the description adds some meaning by specifying 'from a local path' and 'uncompressed .bbmodel'. However, it does not clarify path format (absolute/relative) or whether the extension is required, so it only partially compensates.
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 'Open', the resource '.bbmodel project', and the context 'from a local path in a new Blockbench project tab for inspection or editing'. This effectively distinguishes it from sibling tools like blockbench_create_rig or blockbench_export_model.
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 when one wants to inspect or edit an existing .bbmodel file, but it does not explicitly state when not to use it or mention alternative tools. No exclusions or prerequisites are provided, limiting guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It lists included features (semantic rigging, textures, locators, animation set) but does not mention side effects, mutation behavior, or whether a project must be open. The description is informative but lacks completeness.
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 two sentences, front-loaded with the core action, and includes essential details without extraneous text. Every component earns its place.
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 complexity (7 parameters, nested colors, enums) and lack of output schema, the description covers high-level purpose and included features. However, it does not explain return values, parameter behavior, or how to choose between this tool and siblings, leaving gaps 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 0%, so the description must compensate. It mentions accessory and animation_set generically but does not explain the colors object, format, or other parameters beyond a brief list. While parameter names and enums are somewhat self-explanatory, the description adds limited value in clarifying their usage or constraints.
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 generates and applies a polished layered chibi pet, listing specific species and components like rigging, details, and animation set. It is distinct from sibling tools like blockbench_create_rig or blockbench_build_from_reference, which target different model creation tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for creating chibi pets from an art-direction brief but does not explicitly state when to use this tool over alternatives like blockbench_create_rig or blockbench_build_from_reference. No guidance on prerequisites or exclusions is provided.
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 must fully convey behavioral traits. It discloses that dry_run validates without side effects, and that the caller must translate images (no vision capability). However, it omits whether the tool creates or overwrites projects, error handling, authentication needs, or rate limits. The description adds some context but leaves gaps.
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 paragraph that efficiently packs the purpose, prerequisites, usage pattern, and supported geometry types. It is front-loaded with the core action. While it could benefit from bullet points for readability, it contains no fluff and every sentence adds value.
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 high complexity (nested blueprint, 8 geometry kinds, animations), the description provides a good overview but lacks details on return values (no output schema), success/failure indicators, and post-build state. The instruction to capture turntable and patch discrepancies implies the result is visual, but completeness is moderate.
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 50%, and the description adds high-level context by listing blueprint components (palette, bone hierarchy, geometry primitives, etc.). However, it does not detail individual nested properties or provide examples. The description complements the schema but does not fully compensate for the missing 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 the tool builds a Blockbench model from an image reference, specifying that the caller must translate the image into a structured blueprint. It lists supported geometry primitives and distinguishes from sibling tools like blockbench_create_rig or blockbench_create_pet by focusing on image-driven reconstruction.
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 actionable guidelines: use dry_run=true first for complex references, and after a successful build, capture a four-view turntable and patch discrepancies. It implicitly warns that the tool does not process images itself, requiring the caller to provide the blueprint. However, it does not explicitly state when not to use this tool or compare with alternatives.
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 full burden. It describes the basic behavior but does not disclose side effects, permissions, or return value details. Minimal transparency beyond the stated purpose.
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?
Single sentence, 12 words, front-loaded with action. No wasted information highly efficient.
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 simple zero-parameter tool, the description is mostly complete. However, it lacks detail about the return value or output format, which might be expected for a status check, but overall adequate.
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 has no parameters, so the description adds meaning by explaining what the tool does. Schema coverage is trivially 100%, but the description is the sole source of semantic 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 checks connectivity and inspects the current project. It uses specific verbs and resources, and distinguishes from siblings like blockbench_get_project by focusing on status rather than just retrieving data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking plugin connection and project inspection, but lacks explicit when-to-use or alternatives. No guidance on when not to use it compared to other Blockbench tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![mcpmodel MCP server](https://glama.ai/mcp/servers/haotranq1234/mcpmodel/badges/card.svg)](https://glama.ai/mcp/servers/haotranq1234/mcpmodel)

Score Badge

Copy to your README.md:

[![mcpmodel MCP server](https://glama.ai/mcp/servers/haotranq1234/mcpmodel/badges/score.svg)](https://glama.ai/mcp/servers/haotranq1234/mcpmodel)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/haotranq1234/mcpmodel'

If you have feedback or need assistance with the MCP directory API, please join our Discord server