dat-ai-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but dat_chat and dat_completions are very similar (both chat endpoints with same features), which could cause misselection. The descriptions help differentiate, but overlap exists.
Naming Consistency5/5
All tools share the 'dat_' prefix and follow a verb or verb_noun pattern. Async status tools use 'status' suffix. The naming is consistent and predictable.
Tool Count5/5
7 tools is well-scoped for an AI service covering browsing, chat, and transcription. Each tool serves a clear purpose without redundancy.
Completeness5/5
The tool set covers all core features: browsing (sync/async, status, screenshot), chat (two API styles), and transcription (sync/async, status). No obvious gaps for the stated domain.
Average 3.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 30 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
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provide safety or behavioral cues. Description mentions 'completed' prerequisite but does not disclose potential errors, size limits, or whether multiple calls are safe. Basic behavioral info is present but incomplete.
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 main action. No wasted words, though could be slightly more structured (e.g., separate output format).
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 download tool with no output schema, description covers core function and params but misses details like base64 encoding format, error handling, or any prerequisites beyond task completion.
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% with both parameters described. Description adds minimal value beyond schema, only hinting to get filenames from browsing result. Baseline score appropriate.
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 (download), resource (screenshot from browsing task), and output format (base64). Distinguishes from sibling tools like dat_browse (starts task) and dat_browse_status (checks status).
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 use after task completion but lacks explicit guidance on when to use vs alternatives, such as referencing dat_browse_status to check completion first.
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 behavioral traits. It only states the purpose, omitting details like whether the tool is read-only, what the response format is, or any polling 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?
The description is a single sentence, fully front-loaded, with no unnecessary words. It efficiently conveys the essential 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 status-check tool with one parameter and no output schema, the description is adequate but minimal. It does not explain what the response contains (e.g., possible statuses), leaving some uncertainty about the tool's output.
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 input schema already documents the single parameter 'task_id' with a clear description. The tool description adds no further meaning beyond what the schema provides, warranting the baseline score.
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 ('check') and resource ('status of an async dat.ai transcription task'), distinguishing it from sibling tools like 'dat_transcribe' (which initiates tasks) and other browsing/completion tools.
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 mentioning 'async dat.ai transcription task,' indicating it should be used after 'dat_transcribe' with async=true. However, it lacks explicit when-not-to-use guidance or alternatives.
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?
Describes key behaviors (NDJSON streaming, built-in tools, tool-streaming incompatibility) but with no annotations, misses return format and error details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise, front-loaded sentences, each adding essential detail with no redundancy.
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 chat tool but lacks output schema description and doesn't cover error cases like enabling tools with streaming, leaving some gaps for agent understanding.
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 covers all parameters with 100% description coverage; description adds value for streaming/tools interplay but doesn't enrich individual parameter meanings 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?
The description clearly identifies the tool as an Ollama-compatible chat endpoint via dat.ai, distinguishes it from sibling browsing/transcription tools by its chat function.
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 usage context: streaming vs non-streaming, and the constraint that tools cannot be used with streaming. However, lacks explicit differentiation from sibling dat_completions.
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 provided; description covers return values and statuses but omits idempotency, rate limits, or whether results are consumed. For a simple status checker, this is acceptable but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, no fluff. Front-loaded with verb and resource, then details. Every sentence adds value.
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 single-parameter, no-output-schema tool, the description covers purpose, usage context, and return types. Minor gaps like polling behavior or error handling exist but are not critical given tool simplicity.
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 100% schema description coverage, the description reinforces the schema but adds little new semantic meaning. It echoes the schema's mention of async=true context, but does not elaborate on format 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 checks status of an async browsing task and lists possible statuses, distinguishing it from sibling tools like dat_browse (initiates) and dat_transcribe_status (for transcription).
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 notes the task_id comes from dat_browse with async=true, providing a usage hint. However, it does not explicitly state when not to use or compare to other status tools like dat_transcribe_status.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool uses sync mode and waits up to 10 minutes, which is important behavioral info. However, it lacks details on rate limits, authentication requirements, error handling, or what happens on timeout, leaving gaps in transparency.
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 concise (three sentences) and front-loaded with the core purpose. Each sentence adds essential information without unnecessary elaboration, making it easy for an AI agent to quickly grasp the tool's functionality.
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 has 4 parameters, no output schema, and no annotations, the description covers the key aspects: input methods, sync/async behavior, and timeout. It does not detail the output format beyond 'transcribed text' or error behavior, but for a straightforward transcribe tool, this is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining that audio_url is fetched by the server and audio_base64 is sent as raw bytes, and that async defaults to false. However, it does not provide format or validation details beyond what the schema already describes, so no extra semantic enrichment.
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: transcribe audio to text using dat.ai's Whisper API. It specifies the two methods of providing audio (URL or base64-encoded data) and distinguishes itself from siblings like dat_transcribe_status for polling, making its role unambiguous.
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 clear context on when to use sync mode (default) vs async mode (by setting async=true and polling with dat_transcribe_status). It also explains the two ways to provide audio input. However, it does not explicitly state when not to use the tool or mention alternatives beyond the sync/async choice.
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?
No annotations are provided, so the description carries full burden. It discloses sync/async behavior, default mode, return format (result and screenshot URLs), and mentions optional parameters that affect behavior (fanout, country routing, session grouping, screenshot modes). It does not mention destructive potential or authentication needs, but the nature of the tool implies read/write possibilities in browsing. The description adds value beyond the schema by explaining the default wait time and polling pattern.
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 three sentences, each serving a distinct purpose: stating the action, describing output, and explaining modes. It is front-loaded with the core functionality and highly efficient with zero wasted words.
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 description covers the main task and sync/async modes, it lacks details on error handling, return value structure (since no output schema is provided), rate limits, and authentication. For a tool with 8 parameters and no output schema, the description should provide more context on expected results and edge cases to fully inform an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description only briefly mentions async mode and timeouts (up to 10 min in sync), adding marginal semantic value beyond parameter descriptions. It does not elaborate on other parameters like fanout, country_iso, or screenshots_mode in the prose itself.
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 runs browser automation tasks from natural language instructions on dat.ai, and specifies that it returns results and screenshot URLs. It distinguishes sync and async modes but doesn't explicitly differentiate from sibling tools, though the role is evident.
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 clear guidance on when to use sync vs async mode, including details that sync waits up to 10 minutes and async returns an ID immediately for polling with dat_browse_status. However, it does not discuss when to choose this tool over siblings like dat_browse_screenshot, though the sibling relationship suggests they are complementary rather than alternatives.
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 provided, so the description must disclose behavior. It covers streaming and tool restrictions, but does not mention other aspects like auth needs or response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with no redundancy. The first sentence immediately conveys the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers key constraints (streaming, tools) but lacks explanation of return values, e.g., streaming response format. Still adequate given tool 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%, so baseline 3. The description explains that datai_tools cannot be used with streaming, adding value beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides OpenAI-compatible chat completions via dat.ai, distinguishing it from sibling tools like dat_browse and dat_transcribe which serve 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It mentions support for streaming/non-streaming and a constraint on using built-in tools with streaming, but does not explicitly guide when to prefer this tool over alternatives like dat_chat.
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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