DeepMiro

Server Quality Checklist

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Latest release: v1.7.16
Disambiguation5/5
Each tool targets a distinct aspect of simulation management: lifecycle (create, cancel, status), data retrieval (simulation_data, get_report), interaction (interview_agent), search/list, and document upload. No overlapping purposes, clear boundaries.
Naming Consistency4/5
Most tools follow a verb_noun pattern in snake_case (e.g., create_simulation, cancel_simulation), but simulation_data and simulation_status are noun-based, creating a slight inconsistency. Overall pattern is clear and readable.
Tool Count5/5
With 9 tools, the set is well-scoped for the domain of swarm simulation management. Each tool serves a necessary function without bloat or redundancy.
Completeness5/5
The tool set covers the full lifecycle: document upload, simulation creation, status monitoring, cancellation, data access, report generation, agent interviews, and search/list. No obvious gaps for the stated purpose.
Average 4.2/5 across 9 of 9 tools scored. Lowest: 3.2/5.
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 May 21, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under AGPL 3.0.
This repository includes a README.md file.
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}
```
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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?
Annotations already indicate readOnlyHint=true and destructiveHint=false, establishing safety. The description adds the scope 'past simulation runs' but does not disclose additional behavioral traits such as default ordering, pagination, or whether it returns all runs or only a subset. With annotations covering the basic safety profile, a score of 3 is appropriate.
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 with no wasted words. It could be slightly expanded with key details, but it is appropriately sized and front-loaded. Minor room for improvement keeps it at 4.
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 absence of an output schema, the description should clarify return values beyond 'status and metadata'. It lacks details on response structure, pagination, or field examples. However, for a straightforward list tool, the description is minimally adequate. Score 3 reflects this gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single optional 'limit' parameter, which includes a description in the schema. The tool description does not mention the parameter, so it adds no value beyond the schema. Baseline 3 is correct.
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 function: listing past simulation runs with status and metadata. It uses a specific verb and resource, distinguishing it from siblings like search_simulations which likely has filtering capabilities. However, it does not explicitly contrast with sibling tools, preventing a perfect score.
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 search_simulations or simulation_status. There is no mention of prerequisites, filters, or context for usage, leaving the agent without decision-making support.
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?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds matching fields but does not disclose search behavior (e.g., wildcards, case sensitivity, pagination). Minimal added value.
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, front-loaded with verb and resource, no unnecessary words. Ideally concise.
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?
Adequate for a simple tool, but lacks return format details (e.g., list of IDs or full objects). With no output schema, description should partially compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description essentially restates what the schema already says for the query parameter. No additional semantic value 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 verb 'search', resource 'simulations', and the matching criteria ('by topic, project name, or simulation ID'), effectively distinguishing from sibling tools like list_simulations.
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 for finding specific simulations, but no explicit guidance on when to prefer this over list_simulations or other siblings. No exclusions or alternatives mentioned.
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?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds pagination behavior, which is useful but does not elaborate on other aspects like rate limits or error handling. No contradictions with annotations.
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: the first lists all data types concisely, the second provides pagination guidance. It is front-loaded, efficient, and every word earned its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 7 parameters and no output schema, the description covers the tool's purpose and pagination well. It lacks details on return format or error handling, but for a data-access tool with comprehensive schema descriptions, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter well-described. The tool description adds minimal parameter-level insight beyond the schema (e.g., pagination hint for offset). Thus, the description provides little extra value for parameter semantics.
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 accesses simulation data and enumerates all data types (profiles, config, actions, posts, timeline, etc.). This specificity distinguishes it from sibling tools like cancel_simulation or create_simulation, making the purpose unmistakable.
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 mentions pagination but does not explicitly guide when to use this tool versus alternatives. It implies it's the read-only data retrieval tool among siblings, but lacks explicit 'when-not-to-use' or references to other tools.
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?
Annotations already declare readOnlyHint true and destructiveHint false. The description adds that the agent responds in character, but does not disclose any additional behavioral traits such as rate limits or session behavior.
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?
Two concise sentences, front-loaded with purpose, no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a simple chat interface with annotations and schema coverage, the description adequately conveys core functionality. No output schema exists, but the response behavior is implied.
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?
Input schema has 100% description coverage for all 4 parameters. The description restates the overall purpose but adds minimal parameter-specific meaning 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 states the verb 'chat' and the resource 'specific simulated agent', with a clear purpose to understand perspective, reasoning, and predicted behavior. It distinguishes from siblings like create_simulation or get_report.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating the agent responds in character based on persona and simulation, but lacks explicit when-not-to-use or alternative tool comparisons. Clear context, no exclusions.
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?
Beyond annotations (readOnlyHint=false), the description discloses server-side text extraction (PyMuPDF for PDFs), return of a document_id, and limits. No contradictions found.
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?
Concise, front-loaded purpose, two sentences with all critical details (limits, behavior, sibling context). No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and good annotations, the description covers purpose, usage constraints, processing behavior, and relationship to create_simulation. Minor gaps in error handling but complete for typical usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and already provides detailed descriptions for file_path. The description reiterates constraints but adds no new parameter meaning 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 'Upload a document for use in simulations' with a specific verb and resource, and distinguishes from sibling tools by noting it returns a document_id for create_simulation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides size and format limits (Max 10MB, PDF/MD/TXT only), and specifies transport dependency ('only works with local file paths... For remote/hosted mode, the client skill uploads via HTTP'), guiding when to use and when not.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds details beyond annotations: mentions SIGTERM, immediate stop, preservation of partial action log, and that post-cancellation calls to get_report or simulation_data are still valid. Annotations already indicate destructiveness, but description elaborates.
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?
Two sentences: first states action and mechanism, second lists use cases. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a simple cancellation tool. Explains behavior, side effects, and post-cancellation capabilities. Output schema not needed.
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?
Only one parameter with full schema coverage. Description does not add meaning beyond the schema's description, which is adequate given simplicity.
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 verb 'Stop a running simulation' and resource 'simulation'. It distinguishes from siblings like create_simulation and get_report by focusing on cancellation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly specifies when to use: 'when a simulation is taking too long, was started by mistake, or is producing bad output you want to abort.' Provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that triggering generation may take 1-3 minutes, caching behavior, and force_regenerate effect. Annotations (readOnlyHint=false, destructiveHint=false) do not cover latency or caching, so description adds significant behavioral context without contradiction.
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?
Two concise sentences: first covers main action and latency, second covers caching and optional parameter. No redundant words, all information earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, description specifies return format ('detailed markdown analysis') and display location ('artifact in the side panel'). Also covers latency, caching, and trigger behavior. Complete for tool's complexity.
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?
Schema coverage is 100%, but description adds rich context: force_regenerate explains invalidation and use cases ('after backend prompt or validator changes'), and mentions caching default. simulation_id is minimally described in schema but tool context implies it must be completed.
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: 'Generate and retrieve the prediction report for a completed simulation.' It specifies the action (generate/retrieve) and resource (report for simulation), and distinguishes it from sibling tools, none of which handle reports.
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?
Provides clear context: use for completed simulations, generation may take 1-3 minutes, and explains when to use force_regenerate (after backend changes). Does not explicitly exclude incomplete simulations, but it's implied. No alternatives mentioned, but no sibling handles reports.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds significant behavioral context: long-polling (blocks up to 50s), the full lifecycle (CREATED → ... → COMPLETED/FAILED), and that COMPLETED returns inline report. This goes well beyond annotations.
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?
Two concise paragraphs: first explains core functionality and long-polling, second lists lifecycle states. Every sentence adds value, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description thoroughly covers what the tool returns: state, and if COMPLETED, full prediction report. It also explains the lifecycle and long-polling behavior. Complete for a status-checking 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?
Input schema covers 100% of parameters with descriptions. The description reinforces the meaning of 'wait' (default true, long-poll) and implies usage of 'detailed'. While schema does most of the work, the description adds context about long-poll behavior and default.
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: 'Check the progress of a running or completed simulation.' It specifies the resource (simulation) and action (check progress), distinguishing it from siblings like get_report or list_simulations. The mention of long-polling and lifecycle adds depth.
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 explains when to use: to check progress, especially with long-polling waiting for state changes. It notes that when state=COMPLETED, the full prediction report is included, which may overlap with get_report. No explicit when-not-to-use is given, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations: it mentions returning immediately with simulation_id and the need to poll simulation_status (blocking up to 50s). It also explains the enrichment and document workflow, which is not evident from annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that front-loads the main purpose and then provides important usage instructions. It is mostly concise, though the 'IMPORTANT' section is lengthy but justified by the critical enrichment steps.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no output schema), the description covers the main workflow: enrichment, document handling, immediate return, and polling. It lacks error handling details but is sufficient for correct invocation.
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?
With 100% schema coverage, the description adds extra meaning: it reinforces prompt enrichment, explains document_id as output of upload_document, and clarifies the simulation flow. This significantly enhances parameter understanding.
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 'Run a swarm prediction — graph build, persona generation, multi-agent simulation, report.' This provides a specific verb and resource, distinguishing it from sibling tools like simulation_status or cancel_simulation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs to enrich the prompt, show it to the user for confirmation, and call upload_document first if documents are provided. It also explains to call simulation_status for completion, giving clear guidance on when and how to use the tool and alternatives.
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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