gemini-diagram-mcp
Server Quality Checklist
Latest release: v1.1.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: prepare for guidance, generate for creation, and refine for modification. There is no overlap in functionality, making it easy for an agent to select the right tool.
Naming Consistency5/5All tool names follow the same verb_noun pattern with underscores: prepare_image, generate_image, refine_image. This consistent naming convention makes the tool set predictable and easy to navigate.
Tool Count5/5With only three tools, the server is well-scoped for its purpose of diagram generation. Each tool covers an essential step in the workflow without unnecessary bloat, and the count is appropriate for a focused utility.
Completeness5/5The tool set covers the full generation lifecycle: prepare, generate, and refine. This provides a complete workflow for users, and there are no obvious missing operations that would cause agent failures.
Average 3.9/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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 MIT License.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It adds useful context about auto-detecting type and asking clarifying questions when uncertain. However, it does not disclose other important behaviors such as output handling, potential side effects, or that 'user_approval' is required for certain inputs, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at three sentences, front-loading the main purpose in the first sentence. The list of types is somewhat redundant with the schema but serves as a quick reference. No unnecessary fluff, though it could be tighter by dropping the redundant list.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 8 parameters and no output schema, the description covers the core purpose and a behavioral trait but lacks information about return values, when not to use it, and how it compares to sibling tools. The presence of siblings makes this incompleteness more impactful, so a score of 3 is appropriate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all parameter meanings. The description adds a list of supported types that mirrors the 'type' enum but does not provide extra semantics beyond the schema. This is a standard baseline when the schema is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Generate' and the resource 'a diagram, chart, or visualization using Gemini', and lists the supported types. This distinguishes it from sibling tools like 'refine_image' and 'prepare_image', which imply modification or preparation rather than creation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for generating diagrams/charts and mentions intelligent type detection plus clarifying questions, but it does not provide explicit guidance on when to choose this tool over siblings, nor does it mention any exclusions or prerequisites. Sibling tools exist, making this gap notable.
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 only states that it refines with modifications, but omits critical side effects such as whether the original is replaced, whether a new image is returned, or what happens if no last image exists. This is a meaningful gap for a tool that mutates prior state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that is front-loaded with the verb and resource. Every word earns its place, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description is functionally adequate but lacks key contextual details like the effect on the previous image and prerequisites. It doesn't explain the behavior fully, but the schema and simplicity make it minimally viable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the sole parameter 'refinement' with a clear description. The tool description adds no extra semantic value beyond what the schema already provides, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Refine' with the clear resource 'the last generated image', distinguishing it from sibling tools like generate_image (creates new) and prepare_image (likely prepares). The phrase 'with modifications' further clarifies scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'the last generated image' clearly implies this tool should be used after an image has been generated, providing contextual guidance. It doesn't explicitly name alternatives or state when not to use it, but the context is sufficiently clear for a tool with this simple role.
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 the full burden. It discloses the behavior: returns guidance, recommendations, and a polished prompt. It sets expectations about the purpose (avoid wasted calls). It does not mention side effects, but for a guidance tool this is less critical.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action ('Get guidance before generating an image'). Every sentence earns its place, including the explicit 'Call this FIRST' instruction and the benefit statement.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two optional params and no output schema, the description covers the essential context: purpose, timing, outputs, and rationale. It is complete for an agent to decide when and how to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the two optional parameters, so the baseline is 3. The description adds minor context by mentioning 'prompt recommendations' and 'polished version', but does not provide syntax or format details beyond what the schema already includes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb and resource: 'Get guidance before generating an image.' It distinguishes itself from siblings by positioning as a pre-generation step ('Call this FIRST') and describing specific outputs (supported parameters, prompt recommendations, polished prompt).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly instructs to 'Call this FIRST' and explains the context (before generating an image) and benefit (avoids rejected generations). However, it does not explicitly name alternatives or state when not to use, though the sibling names imply generation vs. preparation.
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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