Fujitsu Social Digital Twin MCP Server

by 3a3

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between get_metrics, get_simulation_result, and get_simdata that could cause confusion. The descriptions clarify differences, but an agent might struggle to choose the right one for retrieving specific simulation outputs.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, such as analyze_traffic_simulation and list_simulations. The naming is predictable and readable throughout the set.
Tool Count5/5
With 10 tools, the count is well-scoped for a traffic simulation domain, covering configuration, execution, analysis, and listing operations. Each tool appears to serve a specific role without redundancy.
Completeness4/5
The tool set provides strong coverage for simulation lifecycle management, including creation, starting, listing, and retrieving results. A minor gap exists in update or delete operations for simulations or datasets, but agents can likely work around this.
Average 2.8/5 across 10 of 10 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
- Last stable release on April 21, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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, the description carries the full burden. It states the tool 'creates and executes,' implying a mutating action, but fails to disclose any side effects, authorization needs, or performance characteristics. The behavioral impact beyond the obvious is opaque.
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 directly states the tool's function. It is concise with no extraneous words. However, the brevity sacrifices necessary detail.
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 the tool's complexity (4 parameters, nested objects, no output schema, no annotations), the description is severely insufficient. It does not explain return values, parameter relationships, or how to handle the uploaded_files object, leaving major gaps 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?
The description only generically mentions 'usecase' and 'uploaded_files' without explaining their format, constraints, or relationship. The simulation_params parameter is entirely omitted, and the schema provides no descriptions (0% coverage), leaving the agent guessing about parameter usage.
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 creates and executes a simulation from a specified use case and uploaded files. It distinguishes itself from siblings like start_simulation by specifying the input source. However, it does not mention the simulation_params parameter, which is a key input, slightly reducing clarity.
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 is provided on when to use this tool versus alternatives like create_natural_language_simulation_config or start_simulation. There are no prerequisites, exclusions, or context hints for selection.
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; description only says 'converts' and 'interprets' without disclosing side effects, persistence, or error handling.
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 that front-loads the purpose with no fluff, though it could be slightly more compact.
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?
Does not describe return value or output format; with no output schema and many sibling tools, more context is needed 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 has 0% description coverage, and the tool description adds no detail about what the 'description' string should contain or the role of 'ctx'.
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?
Description clearly states it converts natural language to structured simulation config, distinguishing from sibling tools that might use different input formats.
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 create_simulation_from_usecase, nor any prerequisites or exclusions.
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 full burden but only states the tool retrieves data. It does not disclose behavioral traits like whether the operation is read-only, has side effects, requires specific permissions, or any rate limits. For a data retrieval tool, at minimum a read-only hint is expected.
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 that front-loads the core purpose. It could be improved by adding more detail without becoming verbose, but as it stands it is efficient and clear.
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 no output schema, no annotations, and 0% parameter coverage, the description is too minimal. It does not specify the exact metrics returned, any constraints on the simulation state, or how to interpret the output. A more complete description would include expected return format or examples.
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 explanation for the two parameters. The required 'simulation_id' parameter is not described at all, and the optional 'ctx' parameter is only defined in the schema's $defs. The description fails to add meaning beyond the schema's basic type information.
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 retrieves comprehensive metrics from completed simulations, listing specific data types (travel statistics, emissions, traffic flow). This distinguishes it from siblings like 'list_simulations' or 'start_simulation', though it does not explicitly contrast with similar data-retrieval tools like 'get_simdata'.
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 is provided on when to use this tool versus alternatives such as 'analyze_traffic_simulation' or 'get_simulation_result'. The description gives no hints about prerequisites, whether the simulation must be completed, or what distinguishes this from 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?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states 'Conducts comprehensive analysis' which implies a read operation, but does not explicitly confirm read-only, destructive potential, authentication needs, or rate limits. The impact on simulation data is unclear.
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 is front-loaded with the action, but it is vague (e.g., 'comprehensive analysis'). It could be more concise while adding useful detail about parameters or output.
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 5 parameters, no annotations, no output schema, and moderate complexity, the description is insufficient. It does not explain what analysis is performed, how results are returned, or how parameters affect the analysis.
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%, meaning the input schema provides only titles and defaults. The description adds no parameter-specific meaning; it only generically refers to 'specified parameters'. Parameters like region, scenario, and time_range remain unexplained.
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 conducts comprehensive analysis on simulation results, providing insights on traffic patterns, bottlenecks, and optimization opportunities. This verb+resource structure is specific and distinguishes it from sibling tools like get_metrics or get_simulation_result.
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 offers no guidance on when to use this tool versus alternatives, nor does it specify when not to use it or mention prerequisites. Sibling tools exist (e.g., compare_scenarios, get_metrics), but no differentiation advice is given.
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 carry the full burden. It states 'retrieves' indicating a read operation, but does not disclose side effects, error behavior, authentication needs, or what happens if the simdata_id does not exist.
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, concise and to the point. It avoids verbosity, but could be structured to include more details without being wordy.
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?
While the tool is simple (single required parameter), the description lacks information about the output format. There is no output schema, so the description should describe what is returned. It mentions contents but not structure. Adequate but not 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 description coverage is 0%. The description does not explain the parameters (simdata_id and ctx). It mentions 'region settings, time ranges, scenario parameters' but these are not parameters of the tool.
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 retrieves the complete configuration and parameter set for a simulation dataset. It lists example contents (region settings, time ranges, scenario parameters). However, it does not explicitly distinguish from sibling tools like list_simdata or get_simulation_result.
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. There is no mention of when not to use it, prerequisites, or context. The usage is only implied by the description.
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 all behavioral traits. It only describes the return content and lacks details on pagination, ordering, rate limits, or whether it returns active/inactive simulations. This is insufficient for an agent to understand its full 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 a single sentence that is front-loaded with the main action and return fields. It is concise, but could be slightly more informative without being wordy. No wasted space.
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 no output schema and a single parameter (ctx), the description lacks completeness. It does not mention if results are paginated, sorted, or how to handle large lists. The agent would need to infer behavior, which is risky.
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 input schema has one parameter (ctx) with no description at the property level (schema description coverage is 0%). The tool description does not explain the parameter's purpose or usage, though the schema's nested definition provides context. The description adds no value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns a comprehensive list of all traffic simulations with specific fields (IDs, names, status, execution timeframes). This distinguishes it from sibling tools like 'list_simdata' which lists simulation data, not simulations.
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 mentions it returns 'all' simulations but gives no guidance on when to use this tool vs alternatives. It does not explain when not to use it, prerequisites, or context. Sibling tools exist for more specific queries.
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 full burden for behavioral disclosure. It states the tool returns data but does not specify if it is read-only, requires special permissions, or has any side effects. Limited transparency.
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 efficiently conveys the core functionality. It is concise without being overly terse, though it could be slightly more 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?
For a simple list tool, the description adequately states the return value. However, given the number of sibling tools, more context on what distinguishes this from similar list operations would improve completeness.
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 only parameter ('ctx') is optional and has no description in the schema (coverage 0%). The tool description does not mention or explain this parameter, leaving the agent without guidance on how to use it.
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 action ('Returns a complete list') and the resource ('simulation datasets'), and the phrase 'available in the system' distinguishes it from sibling tools like list_simulations which list simulations, not datasets. It is specific and unambiguous.
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 its siblings (e.g., list_simulations, get_simdata). It does not mention any prerequisites or conditions for usage.
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 bears full burden for behavioral disclosure. It implies a read-only analysis operation, which is adequate, but does not disclose whether it has side effects, performance implications, or authorization requirements beyond what can be inferred.
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 of 18 words, front-loading the key action and outcome. Every word contributes to the purpose, with no repetition or fluff.
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 5 parameters, no output schema, and no annotations, the description is too brief. It does not explain the output format, interpretation of differences, constraints on input scenarios (e.g., same simulation or not), or error conditions, leaving significant gaps for agent understanding.
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 description adds no meaning to the 5 parameters beyond what the input schema already provides (titles, types, defaults). Since schema description coverage is 0%, the description fails to compensate by explaining parameter roles, format, or relationships.
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: performing comparative analysis between two simulation scenarios, and lists specific metrics (traffic flow, emissions, travel times). The verb 'compare' and resource 'simulation scenarios' are specific and distinguish it from siblings like 'analyze_traffic_simulation' which focuses on a single simulation.
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 is provided on when to use this tool versus alternatives like 'analyze_traffic_simulation' or 'get_simulation_result'. There are no prerequisites, exclusion criteria, or context about when comparison is appropriate.
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; description only states it retrieves results, but does not disclose read-only nature, blocking behavior, or what happens if simulation is not finished. Minimal behavioral context beyond the name.
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 of 16 words, efficient and front-loaded. No wasted words, but could benefit from slight restructuring to mention prerequisites or return type.
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?
Covers main purpose but omits return format, error conditions, prerequisites (simulation must be finished), and relationships to sibling tools. Adequate but with clear gaps for a simple tool.
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 has 0% description coverage, and the description does not explain any parameter meaning. The required 'simulation_id' is not described (e.g., how to obtain it, format), and 'ctx' is not addressed. No value added beyond 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?
Description clearly states the tool retrieves results and output files from a finished simulation, using specific verb 'retrieves' and resource 'complete results and output files'. It distinguishes from siblings like get_metrics and get_simdata by focusing on finished simulations and including status and files.
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?
Implies usage only after simulation is finished ('from a finished traffic simulation'), but provides no explicit when-to-use vs alternatives or when-not-to-use. No mention of sibling tools or exclusion 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 must fully convey behavioral traits. It discloses that the tool returns an ID and status, but does not mention side effects (e.g., asynchronous nature, resource consumption, failure modes), permissions needed, or whether the simulation runs in the background. This is insufficient for an agent to understand the full impact.
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 of 18 words, front-loading the action and result. It contains no redundant information and is efficiently structured.
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 launches a simulation (a potentially long-running and state-changing operation), the description lacks critical details: synchronous vs. asynchronous, error handling, required preconditions (e.g., simdata_id must be valid), and return value structure. The absence of an output schema further burdens the description, making it incomplete.
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 0% schema description coverage, the description must clarify parameters. It implies simdata_id is the 'simulation dataset configuration', adding context beyond the schema's type-only definition. However, the 'ctx' parameter is not explained at all, and its purpose (optional context) is unclear from the description. Partial compensation.
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 launches a new traffic simulation, specifying the action (launches), resource (new traffic simulation), and output (simulation ID and initial status). It effectively distinguishes from sibling tools like analyze or compare, as this is the initialization step.
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 when you have a simulation dataset configuration (simdata_id) and want to start a simulation, but it lacks explicit guidance on when to use it versus alternatives, such as create_simulation_from_usecase (which also creates simulations). No warnings or prerequisites are mentioned.
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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