GetABrain🧠 | Live Human-in-the-Loop MCP for AI Agents

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clear, distinct purpose: topup link generation, balance checking, response retrieval (one-shot vs. waiting), query listing, response rating, and query submission. The descriptions explicitly differentiate overlapping tools like get_responses and wait_for_responses.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_topup_link, get_balance, list_queries). The verbs are descriptive and the naming convention is uniform.
Tool Count5/5
With 7 tools, the server covers the essential operations for a human-in-the-loop system: balance management, query lifecycle (submit, wait, check), listing, and rating. The count feels well-scoped without unnecessary bloat or gaps.
Completeness4/5
The tool set covers the core workflow (submit, wait, get, rate, fund). However, there are minor gaps: no tool to cancel or delete a query, and no ability to manage balance history or refunds. These are not critical but slightly reduce completeness.
Average 4.6/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 10 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, openWorldHint), description adds ordering ('most recent first'), cost ('no cost'), and summary-level scope. It also mentions status filtering capability.
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?
Description is well-structured: purpose, usage, then disambiguation. It is fairly concise, though slightly verbose with multiple sentences. Front-loaded with main action.
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 presence of an output schema and low complexity (2 optional params), the description covers purpose, usage, and disambiguation adequately. No significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with good descriptions for both parameters. The description reinforces filtering use case but does not add meaning beyond schema (e.g., formats, defaults). Baseline 3 is 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?
The description clearly states it lists recent queries in reverse chronological order, using a specific verb ('List') and resource ('GetABrain queries'). It also distinguishes itself from siblings by noting it does not return individual responses.
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?
Provides explicit use cases: getting an overview, recovering a query_id, filtering by status. Also gives clear when-not-to-use guidance: for individual responses, use get_responses or wait_for_responses.
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?
Beyond annotations, the description discloses that it creates a pending Stripe session without charging, requires human to open URL in browser, agent cannot complete payment, and balance updates only after checkout completes. This is consistent with annotations (readOnlyHint=false, openWorldHint=true) and adds critical behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat lengthy but each sentence adds value. It is well-structured with purpose first, then usage, side effects, and disambiguation. Could be slightly more concise but remains effective.
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 the tool's complexity and the presence of an output schema (not shown but indicated), the description covers all essential aspects: purpose, when to use, side effects, human-in-the-loop requirement, and integration workflow for test/live modes.
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% for the single parameter amount_cents. The schema description already includes min/max, example, and unit. The tool description repeats this information without adding new meaning, so baseline 3 is 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?
The description clearly states the tool generates a one-time Stripe Checkout URL for adding funds to prepaid balance. It specifies the verb 'Generate' and resource 'Stripe Checkout URL', and distinguishes from siblings by noting it only mints a payment link and never moves money or blocks.
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 states when to use: when get_balance shows insufficient funds for submit_query or a human asks to add money. Provides disambiguation about what it doesn't do (no money movement, blocking) and includes notes on test-mode vs live-mode.
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?
Annotations already indicate readOnlyHint and openWorldHint. The description adds behavioral details such as 'no side effects,' response fields (mode, auto_reload, URL), and hints for enabling auto_reload, going 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately sized but each sentence adds distinct value: purpose, usage, read-only nature, response details, disambiguation. It is well-structured and front-loaded, though slightly verbose in the disambiguation part.
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 zero parameters, 100% schema coverage, and an output schema, the description is fully complete. It covers what the tool does, when to use, the response structure, and how it differs from siblings.
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?
No parameters exist, so baseline is 4. The description does not need to add parameter info, and it sufficiently explains the output, which is the only relevant aspect.
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 'Check the prepaid balance' and specifies the resource (balance in cents) and its purpose (fund submit_query calls). It also disambiguates from sibling tools by noting it does not list queries or responses.
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 advises when to use: 'Call before submit_query if unsure funds suffice, or whenever a query fails/stalls for balance reasons.' Also mentions it's free in test and live mode, providing clear contextual guidance.
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 significant context beyond annotations: it writes a rating record, returns updated quality score, does not resubmit or modify original, and mentions server-side rewards/suspension. No contradiction with annotations (readOnlyHint=false, destructiveHint=false).
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 front-loaded with the main action and is fairly concise. However, the first sentence is long and could be slightly streamlined without losing clarity.
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 the tool's moderate complexity (4 parameters, output schema exists), the description covers purpose, usage context, side effects, and disambiguation. The existence of an output schema means return values do not need to be explained.
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 the description adds minimal extra meaning. It mentions 'free-text feedback' which matches 'feedback_text', but beyond that, the schema already explains all parameters adequately.
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 uses specific verbs ('rate', 'feed') and clearly states the resource (worker response). It distinguishes from sibling tools 'get_responses' and 'wait_for_responses' by noting it does not fetch new responses.
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 states when to use ('after reviewing a response from get_responses/wait_for_responses'), explains the effect on worker quality and future eligibility, and disambiguates from fetching tools.
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?
Exceeds annotations by detailing cost/side effects, balance deduction, test mode, and asynchronous behavior. No contradiction 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Effectively front-loaded with purpose and examples, but slightly long. Structure is clear with logical sections.
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?
Covers all aspects: usage, parameters, cost, test mode, return behavior, and links to related tools. Adequate for a complex tool with no output schema.
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?
Adds significant context beyond the 100% schema coverage, with examples for content_data types, detailed pricing for bid_amount_cents, and behavior for test mode.
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 submits a structured question to human workers for HITL tasks. It distinguishes from sibling tools like get_responses and wait_for_responses by noting it returns immediately and is the entry point.
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?
Explicitly states when to use (judgment calls, subjective evaluation, etc.) and provides context on test vs live keys. Could more explicitly state when not to use, but the guidelines are 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?
Beyond annotations (readOnlyHint, openWorldHint), the description reveals non-guarantee of completion, return statuses, and cost implications. No contradiction 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?
Concise yet comprehensive: every sentence adds distinct value (purpose, usage, behavior, alternatives). Well-structured and front-loaded with key information.
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 the polling/blocking complexity, the description fully covers behavior, return values, edge cases (timeout), and relation to sibling tools. Output schema exists, so return format explanation is optional.
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 3 applies. The description mentions defaults for max_wait_seconds and min_responses but adds little beyond schema; acceptable but not outstanding.
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 polls for human responses, blocks up to max_wait_seconds, and returns either 'ready' or 'pending'. It explicitly distinguishes from sibling get_responses by noting the active waiting behavior.
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 advises using immediately after submit_query and contrasts with get_responses for one-shot checks. Also mentions read-only/free status and when to call again on pending, providing clear usage boundaries.
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 and openWorldHint, but the description adds valuable context: 'no cost, returns instantly (does not block or retry)' and clarifies that the response may include fewer than required_responses. 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 three well-structured sentences, each adding unique value. Front-loaded with the core action, then usage guidance, then behavioral notes. No extraneous 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?
Given one parameter, full annotations, and an output schema (not shown but referenced), the description covers all needed aspects: purpose, usage, behavior, and distinction from siblings. It is complete for an agent to select and invoke correctly.
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% with a clear description of query_id. The description reinforces that the ID comes from submit_query, which adds context beyond the schema. A minor point: the description could mention the format or source more explicitly, but overall strong.
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 uses strong specific terms like 'One-shot read' and 'fetch...status and...responses,' clearly distinguishing from wait_for_responses. The verb 'fetch' combined with the resource 'query responses' leaves no ambiguity about the tool's function.
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 states when to use this tool ('check progress on demand' or after wait_for_responses reports 'pending') and contrasts it with wait_for_responses by noting it never delays. This provides clear guidance on choosing between siblings.
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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